Analysis method and analyzer

ABSTRACT

Disclosed is an analysis method for a specimen using an analyzer connected to a host computer, the analysis method including: obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell; generating classification information in which each of the cells is classified into a plurality of cell types, by analyzing the feature data with use of an artificial intelligence algorithm and performing classifying; generating a measurement result of the specimen on the basis of the classification information; displaying, on a display part of the analyzer, the measurement result and at least a part of the classification information; and transmitting, to the host computer, output data that includes the measurement result and in which at least a part of the classification information has been removed.

RELATED APPLICATIONS

This application claims priority to Japanese Patent Application No.2021-040831, filed on Mar. 12, 2021, No. 2021-040830, filed on Mar. 12,2021, and No. 2021-040829, filed on Mar. 12, 2021, the entire contentsof which are incorporated herein by reference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to an analysis method and an analyzer foranalyzing cells in a specimen.

2. Description of the Related Art

Japanese Laid-open Patent Publication No. 2016-514267 (translation ofPCT International Application) describes a method for classifying whiteblood cells into subpopulations by using parameters based onfluorescence and light scattering obtained by causing blood cells toflow in a flow cell. In this method, white blood cell subpopulations areclassified on the basis of a plurality of parameters including axiallight loss (ALL), intermediate angle scatters (IAS), fluorescence (FL1),and depolarized side scatter (PSS), which are generated as a result ofapplying light to each blood cell flowing in the flow cell. Theclassified white blood cell subpopulations are displayed on ascattergram.

SUMMARY OF THE INVENTION

The scope of the present invention is defined solely by the appendedclaims, and is not affected to any degree by the statements within thissummary.

In such a conventional cell classification method as disclosed inJapanese Laid-open Patent Publication No. 2016-514267 (translation ofPCT International Application), one cell is alternatively classifiedinto one classification. However, for example, neutrophils that are thesame in terms of type may have different morphological features due tothe difference in maturity, and thus may include some neutrophils thathave morphological features similar to those of immature granulocytes.In the conventional cell classification method, even when a cell thathas a feature of a normal cell such as a neutrophil and a feature of anabnormal cell such as an immature granulocyte is included, such a cellis alternatively classified into one type, which does not lead to moredetailed analysis in some cases. In addition, when the amount ofinformation to be provided to a user in order to enable detailedanalysis when compared with a conventional method is increased, it isalso necessary to cope with a problem of increase in the volume of data.

An analysis method of the present invention is for a specimen using ananalyzer connected to a host computer. The analysis method includes:obtaining, with respect to each of a plurality of cells contained in thespecimen, feature data of the cell; generating classificationinformation in which each of the cells is classified into a plurality ofcell types, by analyzing the feature data with use of an artificialintelligence algorithm and performing classifying; generating ameasurement result of the specimen on the basis of the classificationinformation; displaying, on a display part of the analyzer, themeasurement result and at least a part of the classificationinformation; and transmitting, to the host computer, output data thatincludes the measurement result and in which at least a part of theclassification information has been removed.

An analyzer of the present invention includes: a measurement unitconfigured to, with respect to each of a plurality of cells contained ina specimen, obtain feature data of the cell; a processor configured togenerate classification information in which each of the cells isclassified into a plurality of cell types, by analyzing the feature datawith use of an artificial intelligence algorithm and performingclassifying, the processor being configured to generate a measurementresult of a specimen on the basis of the classification information; anda display part configured to display the measurement result and at leasta part of the classification information. The processor transmits, to ahost computer connected to the analyzer, output data that includes themeasurement result and in which at least a part of the classificationinformation has been removed.

According to the present invention, information that leads to detailedanalysis of a specimen containing a cell that has features of aplurality of cell types can be provided while increase in the datacommunication load to the host computer is avoided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a cell analyzer and a host computer;

FIG. 2 is a block diagram of a measurement unit;

FIG. 3 is a block diagram of a sample preparation part;

FIG. 4 is a schematic diagram of an FCM detection part;

FIG. 5 illustrates a process of generating a digital signal performed byan A/D converter;

FIG. 6 illustrates a structure of feature parameter data;

FIG. 7 is a schematic diagram describing a process of cellclassification based on a deep learning algorithm;

FIG. 8 shows cell types to be analyzed by the deep learning algorithm;

FIG. 9 is a block diagram of a processing unit;

FIG. 10 is a flow chart describing operation of the cell analyzer;

FIG. 11 is a flow chart describing a test result data generationprocess;

FIG. 12 schematically shows a first example of a data structure of testresult data;

FIG. 13 schematically shows a second example of the data structure oftest result data;

FIG. 14 schematically shows a third example of the data structure oftest result data;

FIG. 15 schematically shows a fourth example of the data structure oftest result data;

FIG. 16 schematically shows a fifth example of the data structure oftest result data;

FIG. 17 is a flow chart of a first example of a test result displayingprocess;

FIG. 18 shows an example of a specimen list screen;

FIG. 19 shows an example of a detail screen;

FIG. 20 shows an example of an analysis screen;

FIG. 21 shows an example of the analysis screen;

FIG. 22 shows an example of the analysis screen;

FIG. 23 shows an example of the analysis screen;

FIG. 24 is a flow chart of a second example of the test resultdisplaying process;

FIG. 25 shows an example of the analysis screen;

FIG. 26 shows an example of the analysis screen;

FIG. 27 shows an example of the analysis screen;

FIG. 28 shows an example of the analysis screen;

FIG. 29 is a flow chart of a third example of the test result displayingprocess;

FIG. 30 is a flow chart of a fourth example of the test resultdisplaying process;

FIG. 31 shows an example of the analysis screen;

FIG. 32 shows an example of the analysis screen;

FIG. 33 shows an example of the analysis screen;

FIG. 34 shows an example of the analysis screen;

FIG. 35 shows an example of the analysis screen;

FIG. 36 shows an example of the analysis screen;

FIG. 37 shows an example of the analysis screen;

FIG. 38 shows an example of the analysis screen;

FIG. 39A shows an example of a scattergram;

FIG. 39B shows another example of the scattergram;

FIG. 40 is a flow chart of a fifth example of the test result displayingprocess;

FIG. 41 shows an example of the analysis screen;

FIG. 42 shows an example of the analysis screen;

FIG. 43 is a flow chart of a transmission process to a host computer;

FIG. 44A shows an example of a structure of output data;

FIG. 44B shows an example of the structure of output data;

FIG. 45A shows an example of the structure of output data;

FIG. 45B shows an example of the structure of output data;

FIG. 46A shows an example of a screen for receiving settings regardingoutput data;

FIG. 46B shows an example of the screen for receiving settings regardingoutput data;

FIG. 47 shows a configuration example of a parallel-processingprocessor;

FIG. 48 shows an installation example of the parallel-processingprocessor to the measurement unit;

FIG. 49 shows an installation example of the parallel-processingprocessor to the measurement unit;

FIG. 50 shows an installation example of the parallel-processingprocessor to the measurement unit;

FIG. 51 shows an installation example of the parallel-processingprocessor to the measurement unit;

FIG. 52 shows an outline of arithmetic processes executed by a processorand the parallel-processing processor;

FIG. 53A shows an outline of a matrix operation executed by theparallel-processing processor;

FIG. 53B shows an outline of a matrix operation executed by theparallel-processing processor;

FIG. 54 shows that a plurality of arithmetic processes are executed bythe parallel-processing processor;

FIG. 55A shows an outline of an arithmetic process regarding aconvolution layer;

FIG. 55B shows an outline of an arithmetic process regarding aconvolution layer;

FIG. 56 is a flow chart of cell classification using a deep learningalgorithm performed by the processor and the parallel-processingprocessor;

FIG. 57 is a flow chart of parallel processing execution;

FIG. 58 is a block diagram showing another configuration example of thecell analyzer;

FIG. 59 is a block diagram showing another configuration example of thecell analyzer;

FIG. 60 is a schematic diagram showing control of theparallel-processing processor by the processor in another configurationexample of the cell analyzer;

FIG. 61 is a block diagram showing another configuration example of thecell analyzer;

FIG. 62 is a block diagram showing another configuration example of thecell analyzer;

FIG. 63 is a block diagram showing another configuration example of thecell analyzer;

FIG. 64 is a block diagram showing another configuration example of thecell analyzer;

FIG. 65 is a block diagram showing another configuration example of thecell analyzer;

FIG. 66 is a schematic diagram showing control of theparallel-processing processor by the processor in another configurationexample of the cell analyzer;

FIG. 67 is a block diagram showing another configuration example of thecell analyzer;

FIG. 68 is a block diagram showing another configuration example of thecell analyzer;

FIG. 69 is a block diagram showing another configuration example of thecell analyzer;

FIG. 70A illustrates a structure of a neural network;

FIG. 70B illustrates a structure of the neural network;

FIG. 70C illustrates a structure of the neural network; and

FIG. 71 illustrates training of the neural network.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the outline and an embodiment of the present invention willbe described in detail with reference to the accompanying drawings. Inthe following description and drawings, the same reference characterdenotes the same or similar components, and description regarding thesame or similar components is omitted.

An analysis method described below is a method in which with respect toeach of a plurality of cells contained in a specimen, feature data ofthe cell is obtained; and classification information in which each ofthe cells is classified into a plurality of cell types, by analyzingfeature data with use of an artificial intelligence algorithm andperforming classification, is generated.

The specimen may be a biological sample collected from a subject. Forexample, the biological sample can include peripheral blood such asvenous blood or arterial blood, urine, and body fluid other than bloodand urine. The body fluid other than blood and urine can include bonemarrow fluid, ascites, pleural fluid, cerebrospinal fluid, and the like.Hereinafter, body fluid other than blood and urine may be simplyreferred to as “body fluid”. A blood sample is a sample of which thenumber of cells can be counted and the cell types of the cells can bedetermined, i.e., a sample in a state of containing cells. Preferably,the blood sample is whole blood. Blood is preferably peripheral blood.An example of blood includes peripheral blood collected by using ananticoagulant agent such as ethylenediaminetetraacetate (sodium salt orpotassium salt), heparin sodium, or the like. Peripheral blood may becollected from an artery or may be collected from a vein.

Cell types that are classified according to this analysis method arebased on cell types according to morphological classification, and aredifferent in accordance with the type of the biological sample. When thebiological sample is blood and when the blood has been collected from ahealthy individual, cell types to be determined in the presentembodiment include red blood cell, nucleated cell such as white bloodcell, platelet, and the like, for example. Nucleated cell includesneutrophil, lymphocyte, monocyte, eosinophil, and basophil, for example.Neutrophil includes segmented neutrophil and band neutrophil, forexample. When blood has been collected from a non-healthy individual,nucleated cell includes abnormal cell.

Abnormal cells mean cells that are not usually observed in peripheralblood of a healthy individual. Abnormal cells may include lymphocyticabnormal cells. Lymphocytic abnormal cells may include atypicallymphocytes (reactive lymphocytes), abnormal lymphocytes includingmature lymphoma, and plasma cells, for example. Abnormal cells mayinclude blasts. Blasts include myeloblasts, lymphoblasts,proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts,orthochromatic erythroblasts, promegaloblasts, basophilic megaloblasts,polychromatic megaloblasts, orthochromatic megaloblasts, and the like.Abnormal cells may include megakaryocytes. Abnormal cells may includeimmature granulocytes. Immature granulocytes include promyelocytes,myelocytes, metamyelocyte, and the like, for example.

Abnormal cells may include other abnormal cells that are not containedin peripheral blood of a healthy individual. Examples of abnormal cellsare cells that appear when a person has a disease, and are tumor cells,for example. In the case of the hematopoietic system, examples ofdiseases are: myelodysplastic syndrome; leukemia such as acutemyeloblastic leukemia, acute promyelocytic leukemia, acutemyelomonocytic leukemia, acute monocytic leukemia, erythroleukemia,acute megakaryoblastic leukemia, acute myeloid leukemia, acutelymphoblastic leukemia, lymphoblastic leukemia, chronic myelogenousleukemia, chronic lymphocytic leukemia, or the like; malignant lymphomasuch as Hodgkin's lymphoma, non-Hodgkin's lymphoma, or the like; ormultiple myeloma.

When the biological sample is urine, cell types include, for example,red blood cell, white blood cell, epithelial cell of transitionalepithelium, squamous epithelium, etc., and the like. Abnormal cells caninclude, for example, bacteria, fungi such as filamentous fungi andyeast, tumor cells, and the like.

When the biological sample is body fluid that usually does not containblood components, such as ascites, pleural fluid, or spinal fluid, celltypes can include red blood cell, white blood cell, and large cell, forexample. The “large cell” here means a cell that is separated from aninner membrane of a body cavity or a peritoneum of a viscus, and that islarger than white blood cells. For example, mesothelial cells,histiocytes, tumor cells, and the like correspond to the “large cell”.

When the biological sample is bone marrow fluid, cell types can include,as normal cells, mature blood cell and immature hematopoietic cell. Themature blood cells include, for example, red blood cells, nucleatedcells such as white blood cells, platelets, and the like. Nucleatedcells such as white blood cells include, for example, neutrophils,lymphocytes, plasma cells, monocytes, eosinophils, and basophils.Neutrophils include segmented neutrophils and band neutrophils, forexample. Immature hematopoietic cells include, for example,hematopoietic stem cells, immature granulocytic cells, immature lymphoidcells, immature monocytic cells, immature erythroid cells,megakaryocytic cells, mesenchymal cells, and the like. Immaturegranulocytes can include, for example, cells such as metamyelocytes,myelocytes, promyelocytes, and myeloblasts. Immature lymphoid cellsinclude, for example, lymphoblasts and the like. Immature monocyticcells include monoblasts and the like. Immature erythroid cells include,for example, nucleated erythrocytes such as proerythroblasts, basophilicerythroblasts, polychromatic erythroblasts, orthochromaticerythroblasts, promegaloblasts, basophilic megaloblasts, polychromaticmegaloblasts, and orthochromatic megaloblasts. Megakaryocytic cellsinclude, for example, megakaryoblasts, and the like.

Examples of abnormal cells that can be contained in bone marrow includehematopoietic tumor cells of: myelodysplastic syndrome; leukemia such asacute myeloblastic leukemia, acute promyelocytic leukemia, acutemyelomonocytic leukemia, acute monocytic leukemia, erythroleukemia,acute megakaryoblastic leukemia, acute myeloid leukemia, acutelymphoblastic leukemia, lymphoblastic leukemia, chronic myelogenousleukemia, or chronic lymphocytic leukemia; malignant lymphoma such asHodgkin's lymphoma or non-Hodgkin's lymphoma; or multiple myeloma, whichhave been described above, and metastasized tumor cells of a malignanttumor developed in an organ other than bone marrow.

This analysis method is suitably performed by using a cell analyzerwhich analyzes a specimen containing cells. The cell analyzer mayinclude a measurement unit which obtains, with respect to each of aplurality of cells contained in the specimen, feature data of the cell;and a controller which analyzes the feature data with use of anartificial intelligence algorithm, thereby classifying each of the cellsinto a plurality of cell types. An example of such a cell analyzer willbe described below.

1. Basic Configuration

With reference to FIG. 1, a basic configuration of a cell analyzer isdescribed. FIG. 1 is a schematic diagram showing an appearance of a cellanalyzer 100. The cell analyzer 100 is an apparatus that analyzes aspecimen derived from an organism in accordance with a test ordertransmitted from a host computer 500. The cell analyzer 100 includes ameasurement unit 400 and a processing unit 300. The host computer 500and the cell analyzer 100 are collectively referred to as a test system1000.

2. Configuration of Measurement Unit

With reference to FIG. 2, a configuration of the measurement unit 400 isdescribed. FIG. 2 shows an example of a block diagram of the measurementunit 400. As shown in FIG. 2, the measurement unit 400 includes: aspecimen suction part 450 which suctions a specimen; a samplepreparation part 440 which prepares a measurement sample from thesuctioned specimen; an FCM detection part 410 which detects each bloodcell in the measurement sample; an analog processing part 420 whichprocesses an analog signal outputted from the FCM detection part 410; ameasurement unit controller 480 which converts the signal processed bythe analog processing part 420 into a digital signal and analyzes thedigital signal; and an apparatus mechanism part 430.

FIG. 3 is a schematic diagram for describing the specimen suction part450 and the sample preparation part 440. The specimen suction part 450includes a nozzle 451 for suctioning a blood specimen from a bloodcollection tube T; and a pump 452 for providing a negativepressure/positive pressure to the nozzle 451. The nozzle 451 is moved inupwardly and downwardly by the apparatus mechanism part 430, to beinserted into the blood collection tube T. When the pump 452 provides anegative pressure in a state where the nozzle 451 is inserted in theblood collection tube T, the blood specimen is suctioned via the nozzle451.

The sample preparation part 440 includes five reaction chambers 440 a to440 e. The reaction chambers 440 a to 440 e are used in measurementchannels of DIFF, RET, WPC, PLT-F, and WNR, respectively. Each reactionchamber has connected thereto, via flow paths, a hemolytic agentcontainer containing a hemolytic agent and a staining liquid containercontaining a staining liquid, which serve as reagents for thecorresponding measurement channel. One reaction chamber and reagents (ahemolytic agent and a staining liquid) connected thereto form ameasurement channel. For example, the DIFF measurement channel isconfigured by a DIFF hemolytic agent and a DIFF staining liquid whichserve as DIFF measurement reagents; and the reaction chamber 440 a. Theother measurement channels are configured in similar manners. Here, anexample of a configuration in which one measurement channel includes onehemolytic agent and one staining liquid is shown. However, onemeasurement channel may not necessarily include both of a hemolyticagent and a staining liquid, and a plurality of measurement channels mayshare one reagent.

Through horizontal and up-down movement by the apparatus mechanism part430, the nozzle 451 having suctioned a blood specimen accesses, fromabove, a reaction chamber, among the reaction chambers 440 a to 440 e,that corresponds to a measurement channel that corresponds to an order,and the nozzle 451 discharges the suctioned blood specimen. The samplepreparation part 440 supplies a corresponding hemolytic agent and acorresponding staining liquid to the reaction chamber having the bloodspecimen discharged therein, to mix the blood specimen, the hemolyticagent, and the staining liquid in the reaction chamber, therebypreparing a measurement sample. The prepared measurement sample issupplied from the reaction chamber to the FCM detection part 410 via aflow path, to be subjected to measurement of cells according to flowcytometry.

FIG. 4 shows a configuration example of an optical system of the FCMdetection part 410. As shown in FIG. 4, in measurement performed by aflow cytometer, when each cell contained in a measurement sample passesthrough a flow cell (sheath flow cell) 4113 provided in the flowcytometer, a light source 4111 applies light to a flow cell 4113, andscattered light and fluorescence emitted from the cell in the flow cell4113 due to this light are detected by light-receiving elements 4116,4121, 4122.

In FIG. 4, light emitted from a laser diode being the light source 4111is applied via a light application lens system 4112 to each cell passingthrough the flow cell 4113.

As shown in FIG. 4, forward scattered light emitted from a particlepassing through the flow cell 4113 is received by the light-receivingelement 4116 via a condenser lens 4114 and a pin hole part 4115. Thelight-receiving element 4116 is a photodiode, for example. Sidescattered light is received by a light-receiving element 4121 via acondenser lens 4117, a dichroic mirror 4118, a bandpass filter 4119, anda pin hole part 4120. The light-receiving element 4121 is aphotomultiplier, for example. Side fluorescence is received by alight-receiving element 4122 via the condenser lens 4117 and thedichroic mirror 4118. The light-receiving element 4122 is aphotomultiplier, for example. As the light-receiving element 4116, anavalanche photodiode or a photomultiplier may be used, instead of aphotodiode. As the light-receiving element 4121 and the light-receivingelement 4122, a photodiode or an avalanche photodiode may be used.

Reception light signals outputted from the respective light-receivingelements 4116, 4121, 4122 are inputted to the analog processing part 420via amplifiers 4151, 4152, 4153, respectively. The analog processingpart 420 is connected via signal transmission paths 421 to an A/Dconverter 482 of the measurement unit controller 480 described later.

With reference back to FIG. 2, the analog processing part 420 performsprocesses such as noise removal and smoothing onto each analog signalinputted from the FCM detection part 410, and outputs the processedanalog signal to the measurement unit controller 480.

The measurement unit controller 480 includes the A/D converter 482, aprocessor 4831, a RAM 4834, a storage 4835, a bus controller 4850, aparallel-processing processor 4833, a bus 485, and an interface part 489connected to the processing unit 300. The measurement unit controller480 includes an interface part 484 provided between the bus 485 and theA/D converter 482. The measurement unit controller 480 further includesan interface part 488 provided between various types of hardware (i.e.,the specimen suction part 450, the apparatus mechanism part 430, thesample preparation part 440, the FCM detection part 410) and the bus485.

The A/D converter 482 samples the analog signal outputted from theanalog processing part 420 at a predetermined sampling rate (e.g.,sampling at 1024 points at a 10 nanosecond interval, sampling at 128points at an 80 nanosecond interval, sampling at 64 points at a 160nanosecond interval, or the like), and converts the analog signal into adigital signal. The A/D converter 482 converts the analog signal that isfrom the measurement start of the specimen to the measurement end, intoa digital signal. When a plurality of types of analog signals (e.g.,analog signals respectively corresponding to forward scattered lightintensity, side scattered light intensity, and fluorescence intensity)are generated through measurement in a certain measurement channel, theA/D converter 482 converts each analog signal that is from themeasurement start to the measurement end, into a digital signal. Asdescribed with reference to FIG. 4, three types of analog signals (i.e.,forward scattered light signal, side scattered light signal, andfluorescence signal) are inputted via a plurality of correspondingsignal transmission paths 421 to the A/D converter 482. The A/Dconverter 482 converts each of the analog signals inputted from theplurality of signal transmission paths 421, into a digital signal.

FIG. 5 is a schematic diagram for describing a process of sampling of ananalog signal performed by the A/D converter 482. When a measurementsample containing a cell C is caused to flow in the flow cell 4113, andlight is applied to the flow cell 4113, forward scattered light isgenerated in a forward direction with respect to the advancing directionof light. Similarly, side scattered light and side fluorescence aregenerated to a side direction with respect to the advancing direction oflight. The forward scattered light is received by the light-receivingelement 4116, and a signal corresponding to the amount of the receivedlight is outputted. The side scattered light is received by thelight-receiving element 4121, and a signal corresponding to the amountof the received light is outputted. The side fluorescence is received bythe light-receiving element 4122, and a signal corresponding to theamount of the received light is outputted. In association with passagethrough the flow cell 4113 of a plurality of cells contained in themeasurement sample, an analog signal which represents change in thesignal associated with a lapse of time is outputted from each of thelight-receiving elements 4116, 4121, 4122. The analog signalcorresponding to forward scattered light is referred to as a “forwardscattered light signal”, an analog signal corresponding to sidescattered light is referred to as a “side scattered light signal”, andan analog signal corresponding to side fluorescence is referred to as a“fluorescence signal”. One pulse of each analog signal corresponds toone cell.

The analog signals are inputted to the A/D converter 482. From a startpoint, which is the time point when the level of the forward scatteredlight signal, among the analog signals inputted from the light-receivingelements 4116, 4121, 4122, has reached a level set as a predeterminedthreshold, the A/D converter 482 samples the forward scattered lightsignal, the side scattered light signal, and the fluorescence signal.The A/D converter 482 samples the respective analog signals at apredetermined sampling rate (e.g., sampling at 1024 points at a 10nanosecond interval, sampling at 128 points at an 80 nanosecondinterval, sampling at 64 points at a 160 nanosecond interval, or thelike). The sampling time is fixed irrespective of the size of the pulse.The sampling time is set so as to be longer than the time between therise and fall of the level of the analog signal during passage of onecell through the beam spot of the flow cell 4113. Accordingly, matrixdata that has, as elements, values indicating the analog signal level ata plurality of time points is obtained as a digital signal thatcorresponds to one cell. In this manner, the A/D converter 482 generatesa digital signal of forward scattered light, a digital signal of sidescattered light, and a digital signal of side fluorescence thatcorrespond to one cell. The A/D conversion is repeated until the numberof cells for which the digital signals have been obtained reaches apredetermined number, or until a predetermined time has lapsed from thestart of causing the measurement sample to flow in the flow cell 4113.Accordingly, as shown in FIG. 5, with respect to N cells in themeasurement sample, digital signals obtained by digitizing the waveformsof the analog signals of each cell is obtained. In the presentspecification, a set of sampling data of each cell included in eachdigital signal (in the example in FIG. 5, the set of 1024 digital valuesfrom t=0 ns to t=10240 ns) is referred to as waveform data.

Each piece of waveform data generated by the A/D converter 482 isprovided with an index for identifying the corresponding cell. As theindexes, for example, integers of 1 to N are provided in the order ofthe generated pieces of waveform data, and the waveform data of forwardscattered light, the waveform data of side scattered light, and thewaveform data of side fluorescence are each provided with the sameindex. The index corresponds to a cell ID included in a cell datastructure described later.

Since one piece of waveform data corresponds to one cell, the indexcorresponds to the cell that has been measured. Since an identical indexis provided to the pieces of the waveform data that correspond to thesame cell, a deep learning algorithm described later can analyze thewaveform data of forward scattered light, the waveform data of sidescattered light, and the waveform data of fluorescence that correspondto an individual cell; and classify the type of the cell.

In addition to generation of waveform data that corresponds to eachcell, the A/D converter 482 calculates peak values of pulses of eachsignal, and generates feature parameter data. FIG. 6 is a schematicdiagram showing an example of feature parameter data. In parallel withthe process of converting each analog signal of each cell into digitalvalues in order to generate waveform data, the A/D converter 482sequentially stores the maximum value of digital values included in thewaveform data of each cell from the head column of feature parameterdata. The column N stores the value of the cell for which the N-thwaveform data has been generated. The position of the column matches theindex provided at the head of the waveform data. That is, the digitalvalue corresponding to the waveform data having an index “N” is storedin the column N. The maximum value of the digital values corresponds tothe peak height of the corresponding pulse of the analog signal of thecell. Therefore, with respect to each of the forward scattered lightsignal, the side scattered light signal, and the side fluorescencesignal, through extraction of the maximum value of the digital valuesincluded in the waveform data of each cell, the peak value (referred toas FSCP) of the pulse of the forward scattered light signal, the peakvalue (referred to as SSCP) of the pulse of the side scattered lightsignal, and the peak value (referred to as SFLP) of the pulse of theside fluorescence signal can be obtained for each cell.

With reference back to FIG. 2, the A/D converter 482 inputs thegenerated digital signals and feature parameter data to the bus 485. Thebus controller 4850 transmits the digital signals outputted from the A/Dconverter 482, to the RAM 4834, through DMA (Direct Memory Access)transfer, for example. The RAM 4834 stores the digital signals and thefeature parameter data.

The processor 4831 is connected to the interface part 489, the interfacepart 488, the interface part 484, the RAM 4834, and the storage 4835 viathe bus 485. The processor 4831 is connected to the parallel-processingprocessor 4833 via the bus 485. The processing unit 300 is connected tocomponents of the measurement unit 400 via the interface part 489 andthe bus 485. The bus 485 is a transmission line having a data transferrate of not less than several hundred MB/s. The bus 485 may be atransmission line having a data transfer rate of not less than 1 GB/s,for example. The bus 485 performs data transfer in accordance with thePCI-Express or PCI-X standard, for example.

Using the parallel-processing processor 4833, the processor 4831analyzes each digital signal by executing analysis software 4835 astored in the storage 4835.

The processor 4831 is a CPU (Central Processing Unit), for example. Theparallel-processing processor 4833 executes, in parallel, a plurality ofarithmetic processes which are at least a part of the process regardinganalysis of waveform data. The parallel-processing processor 4833 is aGPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array),or an ASIC (Application Specific Integrated Circuit), for example. Whenthe parallel-processing processor 4833 is an FPGA, theparallel-processing processor 4833 may have programed therein in advancean arithmetic process regarding a deep learning algorithm 60 realized bythe analysis software 4835 a, for example. When the parallel-processingprocessor 4833 is an ASIC, the parallel-processing processor 4833 mayhave incorporated therein in advance a circuit for executing thearithmetic process regarding the deep learning algorithm 60, or may havea programmable module built therein in addition to such an incorporatedcircuit, for example. As the parallel-processing processor 4833,GeForce, Quadro, TITAN, Jetson, or the like of NVIDIA Corporation issuitably used, for example.

FIG. 7 illustrates an arithmetic process of the deep learning algorithm60 of the analysis software 4835 a.

The deep learning algorithm 60 is implemented as a neural network thatincludes a multi-layered middle layer. Preferably, the neural network isa convolutional neural network (CNN) having a convolution layer. Thenumber of nodes of an input layer 60 a in the neural network correspondsto the number of sequences included in the waveform data of one cell tobe inputted. In the example in FIG. 6, the number of nodes of the inputlayer 60 a corresponds to the number of sequences, i.e., 1024×3, of thewaveform data 86 a, 86 b, 86 c of one cell.

An output layer 60 b of the neural network includes the number of nodesthat corresponds to the cell types to be analyzed. In the example inFIG. 8, the cell types to be analyzed are nine types, i.e., “neutrophil(NEUT)”, “lymphocyte (LYMPH)”, “monocyte (MONO)”, “eosinophil (EO)”,“basophil (BASO)”, “immature granulocyte (IG)”, “blast (Blast)”,“abnormal lymphocyte (Abn LYMPH)”, and “not applicable (NONE)”. Thenumber of nodes of the output layer 60 b in this case is nine.

When waveform data has been inputted to the input layer 60 a of theneural network forming the deep learning algorithm 60, a probabilitythat a cell corresponding to the waveform data corresponds to each celltype is outputted from the output layer 60 b, as classificationinformation 82 in which the cell is classified into a plurality of celltypes. The data outputted from the output layer 60 b includes a cell ID,identification information (the label value in FIG. 8) of the cell type,and a numerical value of the probability corresponding to each celltype.

The processor 4831 stores into the RAM 4834 the classificationinformation 82 of the cell obtained by the deep learning algorithm 60.The classification information 82 and the feature parameter data of eachcell is transmitted to the processing unit 300 via the interface part489.

3. Configuration of Processing Unit 300

A configuration of the processing unit 300 is described with referenceto FIG. 9. The processing unit 300 is connected to the processor 4831 ofthe measurement unit 400 via an interface part 3006 and a bus 3003, andcan receive the classification information 82 and the feature parameterdata generated by the measurement unit 400. The interface part 3006 is aUSB interface, for example.

The processing unit 300 includes a processor 3001, the bus 3003, astorage 3004, the interface part 3006, a display part 3015, and anoperation part 3016. The processing unit 300 as hardware is implementedby a general personal computer, and functions as a processing unit ofthe cell analyzer 100, by executing a dedicated program stored in thestorage 3004.

The processor 3001 is a CPU, and can execute a program stored in thestorage 3004.

The storage 3004 includes a hard disk device. The storage 3004 stores atleast a program for processing the classification information 82 of eachcell transmitted from the measurement unit 400 and for generating a testresult of the specimen.

The display part 3015 includes a computer screen. The display part 3015is connected to the processor 3001 via the interface part 3006 and thebus 3003. The display part 3015 can receive an image signal inputtedfrom the processor 3001 and display a test result.

The operation part 3016 includes a pointing device including a keyboard,a mouse, or a touch panel. A user such as a doctor or a laboratorytechnician operates the operation part 3016 to input a measurement orderto the cell analyzer 100, thereby being able to input a measurementinstruction in accordance with the measurement order. The operation part3016 can also receive an instruction of displaying a test result fromthe user. By operating the operation part 3016, the user can viewvarious types of information regarding the test result, such as a graph,a chart, or flag information provided to the specimen.

4. Operation of Cell Analyzer 100

With reference to FIG. 10, a specimen analysis operation performed bythe cell analyzer 100 is described.

When the processor 3001 of the processing unit 300 has received ameasurement order and a measurement instruction from the user via theoperation part 3016, the processor 3001 transmits a measurement commandto the measurement unit 400 (step S1).

Upon receiving the measurement command, the processor 4831 of themeasurement unit 400 starts measurement of a specimen. The processor4831 causes the specimen suction part 450 to suction a specimen from ablood collection tube T (step S10). Next, the processor 4831 causes thespecimen suction part 450 to dispense the suctioned specimen into one ofthe reaction chambers 440 a to 440 e of the sample preparation part 440.The measurement command transmitted from the processing unit 300 in stepS1 includes information of a measurement channel for which measurementis requested by the measurement order. On the basis of the informationof the measurement channel included in the measurement command, theprocessor 4831 controls the specimen suction part 450 so as to dischargethe specimen into the reaction chamber of the corresponding measurementchannel.

The processor 4831 causes the sample preparation part 440 to prepare ameasurement sample (step S11). In step S11, upon receiving an order fromthe processor 4831, the sample preparation part 440 supplies thereagents (hemolytic agent and staining liquid) into the reaction chamberhaving the specimen discharged therein, to mix the specimen with thereagents. Accordingly, a measurement sample in which red blood cells arehemolyzed by the hemolytic agent and in which cells, such as white bloodcells or reticulocytes, serving as the target of the measurement channelare stained by the staining liquid, is prepared in the reaction chamber.

The processor 4831 causes the FCM detection part 410 to measure theprepared measurement sample (step S12). In step S12, the processor 4831controls the apparatus mechanism part 430 to send the measurement samplein the reaction chamber of the sample preparation part 440, to the FCMdetection part 410. The reaction chamber and the FCM detection part 410are connected to each other by a flow path, and the measurement samplesent from the reaction chamber flows in the flow cell 4113, and isirradiated with laser light by the light source 4111 (see FIG. 4).

As a result of the measurement sample being supplied to the flow cell4113, a forward scattered light signal, a side scattered light signal,and a side fluorescence signal are inputted to the A/D converter 482.The A/D converter 482 generates digital signals each being a set ofpieces of waveform data that correspond to individual cells, and featureparameter data storing the maximum value included in the waveform dataof each cell. The methods for generating the waveform data and thefeature parameters have been described above.

The processor 4831 controls the bus controller 4850 to cause thewaveform data and the feature parameter data generated by the A/Dconverter 482 to be taken into the RAM 4834 through DMA transfer.Through the DMA transfer, the waveform data is directly transferred tothe RAM 4834, not via the processor 4831. The waveform data is storedinto the RAM 4834.

By using the deep learning algorithm 60, the processor 4831 executescell classification on the basis of the generated waveform data, andgenerates classification information (step S13).

The processor 4831 transmits, to the processing unit 300, theclassification information 82 and the feature parameter data of eachindividual cell which have been obtained as a result of step S13 (stepS14).

Upon receiving the classification information 82 and the featureparameter data from the measurement unit (step S2), the processor 3001of the processing unit 300 analyzes the classification information 82 byusing a program stored in the storage 3004, and generates test resultdata of the specimen (step S3). The test result data is stored into thestorage 3004. The process of step S3 will be described later.

The processor 3001 displays the test result on the display part 3015(step S4). The process of step S4 will be described later.

The processor 3001 transmits the test result to the host computer 500(step S5). The process of step S5 will be described later.

The host computer 500 receives the test result transmitted from theprocessor 3001 of the processing unit 300 (step S21). Accordingly, aseries of processes ends.

5. Test Result Data Generation Process

FIG. 11 is a flow chart showing details of step S3 (test result datageneration process) executed by the processor 3001 of the processingunit 300.

On the basis of the classification information 82 received from themeasurement unit 400, the processor 3001 determines a main cell type ofthe cell (step S31). Specifically, on the basis of the probability foreach cell type included in the classification information 82, theprocessor 3001 determines, as the main cell type, a cell type that hasthe highest probability. For example, in a case where the probabilitydata obtained with respect to a certain cell is as indicated below, thecell type that has the highest probability is neutrophil, and thus, themain cell type of the cell is neutrophil.

TABLE 1 Cell type Probability neutrophil 90%  lymphocyte 10%  monocyte0% eosinophil 0% basophil 0% immature granulocyte 0% blast 0% abnormallymphocyte 0%

Next, on the basis of the determined main cell type, the processor 4831counts the number of cells contained in the specimen, for each cell type(step S32). In the process of step S32, on the basis of the informationof cell types of each individual cell, the number of cells is countedfor each cell type. For example, when WDF is set as the measurementchannel, the number of cell types to be classified is nine as shown asan example in FIG. 8. For example, when the number of cells of which themain cell type is neutrophil is M, the processor 3001 generates acounting result in which the number of neutrophils is M. The processor3001 performs a similar process on lymphocyte, monocyte, eosinophil,basophil, immature granulocyte, blast, and abnormal lymphocyte, andgenerates a counting result for each cell type.

The processor 3001 may further determine whether the specimen isnormal/abnormal on the basis of the count value of each cell type. Forexample, since blasts are not contained in peripheral blood of a healthyindividual, when not less than a predetermined number of cells of whichthe main cell type has been determined as blast are contained, thespecimen is suspected to have some abnormality. In addition, sinceimmature granulocytes are not usually contained in blood of a healthyindividual, when not less than a predetermined number of cells of whichthe main cell type has been determined as immature granulocyte arecontained, the specimen is suspected to have some abnormality. Theprocessor 3001 compares the count value (absolute value) of each celltype with a predetermined numerical range or threshold, and when thecount value is outside the numerical range or exceeds the threshold, theprocessor 3001 may add a flag indicating that abnormality is suspected,to the analysis result.

The processor 3001 may further obtain the content ratio of cells on thebasis of the count value of each cell type. For example, when WDF is setas the measurement channel, the processor 3001 obtains the ratio of fivewhite blood cell subclasses of neutrophil, lymphocyte, monocyte,eosinophil, and basophil. Blood of a healthy individual contains thesefive white blood cell subclasses at predetermined proportions. Theprocessor 3001 may determine whether the specimen is normal/abnormal bycomparing these proportions of white blood cell subclasses withpredetermined numerical ranges or thresholds. When a specific whiteblood cell ratio is outside a predetermined numerical range or exceeds athreshold, the processor 3001 may add a flag suggesting that thespecimen is abnormal, to the analysis result.

The processor 3001 generates the above-described test result data inassociation with the cell ID (step S33). FIG. 12 schematically shows afirst example of a data structure of test result data generated by theprocessor 3001.

The test result data is configured as a relational database, andincludes a plurality of data items (columns). In the example shown inFIG. 12, subject ID, specimen ID, measurement result, identificationcode of measurement channel, cell ID, identification information of celltype, and probability for each cell type are provided as data items.

The data of “subject ID” stores a subject ID being the information foridentifying the subject from whom the specimen has been collected. Thesubject ID is a multiple-character string composed of a combination ofnumerals and alphabets, for example.

The data of “specimen ID” stores a specimen ID being the information foridentifying the specimen. The specimen ID is a multiple-character stringcomposed of a combination of numerals and alphabets, for example.

The data of “measurement result” stores a measurement result obtained bythe processor 3001 analyzing measurement data on the basis of the maincell type in step S32. The measurement result includes a counting resultof cells based on the cell type (main cell type) that has the highestprobability, as described above. When the measurement channel is WDF,the measurement result includes the number and proportion of each of thewhite blood cell subclasses (monocyte, neutrophil, lymphocyte,eosinophil, basophil). When the measurement channel is RET, themeasurement result includes the number of reticulocytes. When themeasurement channel is WPC, the measurement result includes the numberof hematopoietic progenitor cells. When the measurement channel is WNR,the measurement result includes the number of white blood cells and thenumber of nucleated erythrocytes.

The data of “measurement channel” stores information that indicates themeasurement channel of the measurement having been performed on thespecimen. In the example of the measurement unit 400 of the presentembodiment, DIFF, RET, WPC, PLT-F, and WNR exist as the measurementchannels. In the data items of the measurement channel, a characterstring of “DIFF”, “RET”, “WPC”, “PLT-F”, or WNR” is stored in accordancewith the measurement channel in which the measurement has beenperformed. The information indicating the measurement channel may be acharacter string indicating the measurement channel, or may be a numeralor alphabet assigned to each measurement channel. For example, numeralsmay be assigned such that DIFF=1, RET=2, WPC=3, PLT-F=4, and WNR=5, andthe numeral that corresponds to the used measurement channel may bestored. The data of “measurement channel” stores one or a plurality ofvalues with respect to one specimen, in accordance with the number ofmeasurement channels in which measurement has been performed.

As for the data of “cell ID”, a plurality of pieces of data are createdin accordance with the number of cells detected in the measurement inone measurement channel. The cell ID is a numerical value of a naturalnumber such as 1, 2, 3, . . . , or N. The cell ID matches the index (seeFIG. 5) added to the head of waveform data and to the sequential order(see FIG. 6) of the column of the feature parameter data that correspondto each individual cell described above.

The data of “feature parameter” stores the values of the forwardscattered light peak value (FSCP), the side scattered light peak value(SSCP), and the side fluorescence peak value (SFLP) of the correspondingcell. In the example in FIG. 12, as the data of “feature parameter”,three feature parameters of FSCP, SSCP, and SFLP are created for onecell ID. As described with reference to FIG. 6, in the feature parameterdata, the sequential orders of columns correspond to cell IDs. Forexample, the head column (first column) of the feature parameter datastores a feature parameter that corresponds to the cell having a cell ID“1”. Therefore, the data of FSCP corresponding to cell ID=1 stores avalue “59” in the head column of the FSCP matrix data in the featureparameter data. Similarly, data of SSCP and data of SFLP store a value“30” in the head column of the SSCP matrix data, and a value “134” inthe head column of the SFLP matrix data, respectively.

The data of “cell type” stores information indicating a cell type to beanalyzed. The information indicating a cell type is, for example, alabel value that can specify a cell type as shown in FIG. 8. As for thedata of “cell type”, a plurality of pieces of data are created inaccordance with the number of cell types to be analyzed in themeasurement channel. For example, when the number of cell types to beanalyzed in a measurement channel is nine including “none” as shown inFIG. 8, nine pieces of data of “cell type” are created. Theidentification information may be a character string such as“neutrophil” or “NEUT”, instead of a label value.

As for the data of “probability”, one piece of data is created for dataof one cell type. With respect to the corresponding cell ID andidentification information, the data of “probability” stores, in theform of numerical data, a value of a probability outputted by the deeplearning algorithm 60. For example, when the classification information82 of a target cell is as shown in Table 1 above, “90%” is stored as avariable corresponding to neutrophil, “10%” is stored as a variablecorresponding to lymphocyte, and “0%” is stored as variablescorresponding to the other pieces of identification information.

The processor 3001 generates test result data shown in FIG. 12, andstores the test result data into the storage 3004.

FIG. 13 schematically shows a second example of test result data. In thedata structure of test result data shown in FIG. 13, when compared withthat in FIG. 12, data storing a main flag is added to each piece ofidentification information. In the case of the data structure in FIG.13, the classification information is composed of identificationinformation, probability, and main flag.

The data of “main flag” stores a value of 0 or 1. The processor 3001stores 1 into the main flag that corresponds to the cell type (main celltype) that has the highest probability, and stores 0 into the main flagsthat correspond to the other cell types. With this configuration, theprocessor 3001 need not specify a main cell type for each individualcell on the basis of the probabilities every time using in an arithmeticoperation or outputting the information of the main cell type.Accordingly, speed up of processing can be realized. For example, in ascattergram described later, a plot of each cell is displayed in a colorthat is different for each cell type. In such a case, the processor 3001can, with reference to the main flag, determine the color of the plot ofeach cell, and need not specify the main cell type on the basis of theprobabilities of each individual cell. In the case of the secondexample, when the deep learning algorithm 60 generates classificationinformation 82, the deep learning algorithm 60 may execute a process ofsetting a numerical value in each main flag together with theclassification information 82.

FIG. 14 schematically shows a third example of test result data. In thetest result data shown in FIG. 14, when compared with that in FIG. 12,one piece of data of “main cell type” is added to one cell ID. In thecase of the data structure in FIG. 14, the classification information iscomposed of identification information, probability, and identificationinformation indicating the main cell type.

The data of “main cell type” stores identification information of thecell type (main cell type) that has the highest probability. In thesecond example in FIG. 13, in order to specify the main cell type ofeach individual cell, it is necessary to search the columns storing themain flags. However, in the third example in FIG. 14, with reference tothe data of “main cell type”, the main cell type can be specified.Accordingly, further speed up of processing can be realized.

FIG. 15 schematically shows a fourth example of test result data. In thetest result data shown in FIG. 15, when compared with that in FIG. 12,data of “research flag” is added so as to correspond to each piece ofidentification information. In the case of the data structure in FIG.15, the classification information is composed of identificationinformation, probability, and research flag.

The data of “research flag” stores a value of 0 or 1. The research flagis a flag for distinguishing the cell type to be displayed as a testresult of a reportable item, from the cell type to be displayed in anauxiliary manner as a research item, on a result display screendescribed later. The processor 3001 stores “1” into a research flag thatcorresponds to a cell type having a probability not higher than apredetermined threshold, and stores “0” into a research flag thatcorresponds to a cell type having a probability higher than thisthreshold. When 1 has been set in the research flag, in a case wheretest result data is to be displayed, it is possible to provide theprobability, the counting result, or the like regarding this cell type,with information indicating that these pieces of information arepreferably used for research.

FIG. 16 schematically shows a fifth example of test result data. In thetest result data shown in FIG. 16, when compared with that in FIG. 12,data of “rank” is added to each piece of identification information. Inthe case of the data structure in FIG. 16, the classificationinformation is composed of identification information, probability, andrank.

The data of “rank” stores one of numerals of 1 to m (m: the number ofcell types to be analyzed), which are provided in a descending order ofprobabilities to all of the cell types associated with a cell ID. Theprocessor 4831 calculates ranks in a descending order of probabilities,and stores each calculated rank into the data of “rank” of thecorresponding cell type. In this case, with reference to the data of“rank”, the processor 3001 can smoothly understand not only the celltype that has the highest probability, but also the cell type that hasthe second highest probability, for example.

6. Test Result Displaying Process

FIG. 17 is a flow chart illustrating a first example of a test resultdisplaying process (step S4) executed by the processor 3001.

The processor 3001 displays a specimen list screen 700 (sample explorerscreen) (step S410). The processor 3001 displays a detail screen 800(browser screen) in accordance with an operation by the user (stepS411). In accordance with a display instruction from the user, theprocessor 3001 displays an analysis screen 900 including a scattergram(step S412). The processor 3001 reads out classification information ofa cell that corresponds to a dot selected in a scattergram 901 displayedon the analysis screen 900, and displays the read out classificationinformation (step S413).

FIG. 18 shows an example of the specimen list screen 700. The specimenlist screen 700 includes a tool bar 710 and a data display region 720. Aplurality of icons for performing predetermined operations on the screenare displayed in the tool bar 710. Specifically, the tool bar 710includes: a specimen list screen icon 710 a for calling the specimenlist screen 700 (SE screen); a detail icon 710 b for calling the detailscreen 800; a measurement registration icon 710 c for calling ameasurement order input screen for inputting a measurement order; and avalidation icon 710 d for validating a test result.

The data display region 720 in which data of test results is displayedis provided in a part below the tool bar 710. The tool bar 710 is alwaysdisplayed in an upper part of the screen, irrespective of the contentdisplayed in the data display region 720. A specimen list display region931 and a measurement result display region 932 are displayed in thedata display region 720 of the specimen list screen 700.

The specimen list display region 931 is provided with items such assubject ID, specimen ID, and day and time. In the specimen list displayregion 931, test result data identified by a subject ID and a specimenID is displayed in the form of a list.

A measurement result based on the specimen ID selected in the specimenlist display region 931 is displayed in the measurement result displayregion 932. The measurement result displayed in the measurement resultdisplay region 932 is a counting result and ratio information based onthe main cell type.

When the user has selected the detail icon 710 b on the specimen listscreen 700, or has selected (e.g., double clicked) a record of onespecimen in the specimen list display region 931, a detail screen 800 isdisplayed.

FIG. 19 shows an example of the detail screen 800. The detail screen 800includes a tool bar 810 (710), and a data display region 820. The toolbar 810 is the same as that displayed on the specimen list screen 700. Ameasurement result region 820 a for displaying a measurement result, aflag region 820 b for displaying flag information, and a graph region820 c for displaying a graph are displayed in the data display region820. Measurement results of all of the measurement channels for whichmeasurements have been performed on the specimen to be displayed aredisplayed in the measurement result region 820 a. When a flag has beenset to the specimen on the basis of a measurement result, the flag(suspicion message) is displayed in the flag region 820 b. A graphcorresponding to each measurement channel is displayed in the graphregion 820 c.

In the example in FIG. 19, a WDF scattergram, a WNR scattergram, a WPCscattergram, an RET scattergram, a PLT-F scattergram, an RBC histogram,and a PLT histogram are displayed in the graph region 820 c. Eachscattergram displayed in the graph region 820 c is created on the basisof the feature parameters described above, and is displayed.

The user refers to the analysis result displayed on the detail screen800, and when the user validates test result data, the user operates thevalidation icon 710 d. Accordingly, the test result data is validated.When the user does not validate the test result data, the user mayoperate the measurement registration icon 710 c to input a retest order,for example.

In order to confirm classification information of a cell, the user cancause the analysis screen 900 to be displayed. In the graph region 820 con the detail screen 800, when the user operates (e.g., double clicks) agraph, the analysis screen 900 is displayed in a pop-up manner, to beoverlaid on the detail screen 800.

FIG. 20 is an example of the analysis screen 900. FIG. 20 shows, as anexample, the analysis screen 900 that is displayed when the user hasselected a WDF scattergram.

The scattergram 901 and a detail display region 902 are displayed on theanalysis screen 900. The scattergram 901 shown in FIG. 20 is displayedon the basis of the feature parameters of the cell associated with themeasurement channel included in the test result data. In the scattergram901 shown in FIG. 20, the horizontal axis represents the peak value ofside scattered light signal (SSCP), and the vertical axis represents thepeak value of side fluorescence signal (SFLP). With respect to eachindividual cell specified by a cell ID, the processor 3001 determines acoordinate in the scattergram 901 on the basis of SSCP and SFLP, andrenders a dot on the determined coordinate, thereby plotting the cell.The processor 3001 performs similar processes to the data of all of thecells associated with the measurement channel that corresponds to thescattergram 901 to be displayed, thereby plotting the cells on thescattergram 901.

The processor 3001 sets a color of a dot on the basis of theclassification information of the cell to be plotted. Specifically, theprocessor 3001 displays cells of different cell types, in the form ofdots having different colors. For example, in the scattergram 901corresponding to the WDF channel, neutrophils are displayed in a firstcolor (e.g., blue), lymphocytes are displayed in a second color (e.g.,purple), monocytes are displayed in a third color (e.g., green),eosinophils are displayed in a fourth color (e.g., red), and basophilsare displayed in a fifth color (e.g., yellow).

White blood cell classification performed in a conventional blood cellcounter has adopted a method of clustering analysis in which cells areplotted on a coordinate plane such as the scattergram 901 shown in FIG.20, and in which cells are classified into a plurality of populations onthe basis of the coordinate of each cell. Therefore, cells that havebeen plotted on the same coordinate are classified into the same type ofcell. Meanwhile, in the present embodiment, different from theconventional white blood cell classification, the feature parameters(SSCP and SFLP) of cells are used only for plotting the cells onto thescattergram 901. As described above, cell classification in the presentembodiment uses the deep learning algorithm 60 to analyze waveform dataof each individual cell, thereby individually identifying the type ofeach cell, without using the clustering technique. In other words, thecoordinates of the cell plots in the scattergram 901 are fragmentaryinformation representing the distribution of cells in a measurementsample on the basis of parameters indicating the features of the cells,and the coordinates are not used for identification of cell types.Therefore, even when a plurality of cells are plotted on the samecoordinate, such cells may be classified into different cell types bythe deep learning algorithm 60. When cells of different types have beenplotted on the same coordinate, the processor 3001 renders, on the basisof the main cell types of the plurality of cells plotted on the samecoordinate, a dot of a color that corresponds to the main cell type thathas the highest proportion.

In step S413 of the flow chart in FIG. 17, when the user has performedan operation (e.g., double click) of selecting a dot on the scattergram901, the processor 3001 displays the detail display region 902 in apop-up manner. The detail display region 902 includes a classificationinformation display region 902 a, and the classification informationdisplay region 902 a displays the classification information of the cellincluded in the dot selected on the scattergram 901. FIG. 20 shows anexample in which a plurality of cell types and the probabilitycorresponding to each cell type are displayed. The classificationinformation display region 902 a is provided with a scroll bar forallowing display of all of the cell types and probabilities by changingthe display range in the classification information display region 902a. When a plurality of cells have been plotted on the dot selected onthe scattergram 901, the classification information display region 902 afor each of the plurality of cells is displayed in the detail displayregion 902. By operating the scroll bar, the user can cause theclassification information to be displayed in a scrolling manner.

On the analysis screen 900 in FIG. 20, the classification information ofa cell included in the selected dot is displayed in a pop-up manner.However, as shown in FIG. 21, the classification information displayregion 902 a may be provided in the analysis screen 900. In this case,until a dot is selected, the classification information display region902 a remains blank, and upon selection of a dot, classificationinformation of a cell corresponding to the selected dot is displayed inthe classification information display region 902 a.

In the above example, the detail display region 902 is displayed in apop-up manner in response to an operation (e.g., double click) performedon a dot on the scattergram 901. However, the operation for calling thedetail display region 902 can include various operations as long as theoperation can specify a coordinate on the scattergram 901. For example,the detail display region 902 may be displayed simply by placing acursor on the scattergram 901. In this case, placing the cursor on thescattergram 901 serves as an operation for calling the detail displayregion 902. Instead of a double click, the operation may be a longpress, a right click, or pressing a predetermined key (e.g., Enter key)of a keyboard or a software key in a state where the cursor is placed ona coordinate.

According to the screens in FIG. 20 and FIG. 21, with reference to theclassification information display region 902 a, the user can confirmthe classification information of the cell that corresponds to the dotselected on the scattergram 901. In addition, by changing the dotselected on the scattergram 901, the user can dynamically switch thedisplay content of the classification information display region 902 a,thereby being able to continuously confirm the display content.

In the classification information display region 902 a, not limited tothe probability for each cell type, another type of information may bedisplayed as the classification information. For example, when the testresult data has the configuration shown in FIG. 13, the value of themain flag for each cell type may be displayed. When the test result datahas the configuration shown in FIG. 14, a mark may be displayed at themain cell type (the cell type having the highest probability), or themain cell type may be separately displayed. When the test result datahas the configuration shown in FIG. 15, the value of the research flagfor each cell type may be displayed. When the test result data has theconfiguration shown in FIG. 16, the rank for each cell type may bedisplayed.

In FIG. 20 and FIG. 21, the probability for each cell type is displayedin terms of a numerical value. Instead of this, as shown in FIG. 22 andFIG. 23, a graph according to the probability for each cell type may bedisplayed. FIG. 22 shows another example of the analysis screen 900. Inthe example in FIG. 22, for each cell corresponding to the selected dot,probability information of the cell is displayed in the form of apercentage bar chart in the classification information display region902 a. This percentage bar chart is displayed so as to correspond toeach cell ID, and is displayed such that the values of the probabilitiesfor the respective pieces of identification information are accumulatedto be a total of 100%. Accordingly, the user can visually understand theprobability of each cell type.

FIG. 23 shows another example of the analysis screen 900. Thescattergram 901, the detail display region 902, an enlargement button903, and an enlarged view 904 are displayed on the analysis screen 900in FIG. 23.

When the user has operated the enlargement button 903, a gate 901 a fora reference range is displayed on the scattergram 901. The gate 901 a isfor selecting a desired dot on the scattergram 901 through rangedesignation. The user can change the position of the gate 901 a on thescattergram 901 by performing an operation such as dragging. In theenlarged view 904, a portion of the scattergram 901 in the gate 901 a isdisplayed in an enlarged manner. When the user has performed anoperation of selecting a dot on the enlarged view 904, theclassification information of the selected cell is displayed in theclassification information display region 902 a in the detail displayregion 902, as in the cases of FIG. 20 and FIG. 21.

According to the screen in FIG. 23, the user can set the gate 901 a onthe scattergram 901 to cause the enlarged view 904 to be displayed,thereby being able to easily select the target cell in the enlarged view904. Accordingly, with respect to a cell in a range where the dots ofcells are densely present in the scattergram 901 as well, theclassification information can be smoothly confirmed.

The size of the gate 901 a may be changed by performing an operationsuch as dragging on the boundary line of the gate 901 a. The size of thegate 901 a may be changed stepwise every time an operation is performedon the enlargement button 903.

In each of FIG. 20 to FIG. 23, an example of selecting one dot on thescattergram 901 is shown. However, the number of dots selectable at onetime is not limited to one. For example, a plurality of dots may beselected through range designation on the scattergram 901, by dragging acursor while clicking a point on the scattergram 901. However, when aplurality of dots are selected at one time, the number of cells to bedisplayed may become too large. This may cause difficulty for the userto view the classification information when the classificationinformation is individually displayed for each cell. Therefore, as willbe later described with reference to FIG. 29, the display method may beswitched in accordance with the number of selected dots. For example,when the number of selected dots is not greater than a predeterminedthreshold, pieces of the classification information of cells may beindividually displayed, and when the number of selected dots is greaterthan the predetermined threshold, the classification information may bestatistically displayed, instead of being individual displayed.

FIG. 24 is a flow chart showing a second example of the test resultdisplaying process. In FIG. 24, when compared with the flow chart inFIG. 17, step S413 has been changed. In step S413 in FIG. 24, theclassification information of each of the cells corresponding to the dotselected on the scattergram 901 is read out, and is displayed asstatistic information on the analysis screen 900.

In the first example of the test result displaying process in FIG. 17,as shown in the examples in FIG. 20 to FIG. 23, the classificationinformation of each of cells corresponding to the selected dot isindividually displayed for each cell. Instead of this, in the examplesin FIG. 25 to FIG. 28 shown below, pieces of the classificationinformation of a plurality of cells corresponding to the selected dotare aggregated, and statistic information is displayed. Examples of theanalysis screen 900 are shown in FIG. 25 to FIG. 28.

The scattergram 901, the enlargement button 903, the enlarged view 904,and a detail display region 905 are displayed on the analysis screen 900in FIG. 25.

On the basis of the classification information of all of the cells inthe gate 901 a, statistic information of the probability and the numberof cells for each cell type is displayed in the form of a histogram 905a in the detail display region 905. The histogram 905 a is frequencydistribution information obtained by subjecting all of the cells in thegate 901 a to counting for each probability in the cell type. In thehistogram 905 a, the horizontal axis represents probability and thevertical axis represents the number of cells. The numbers shown in thehistogram 905 a include a counting result of cells that are not of themain cell type (cells that have a low probability of belonging to thecell type). The detail display region 905 is provided with a scroll barfor allowing display of the histogram 905 a of all of the cell types bychanging the display range in the detail display region 905.

According to the screen shown in FIG. 25, by causing the histogram 905 afor each cell type to be displayed in the detail display region 905, theuser can understand the distribution of the probabilities for each celltype, with respect to all of the cells in the gate 901 a. For example,in the conventional clustering technique using a scattergram, there arecases where distribution ranges of a plurality of cell types overlapeach other on the scattergram 901. In contrast to this, according to thepresent embodiment, when the gate 901 a is set to such a range where thedistributions overlap each other, and then the histogram 905 a isreferred to, the frequency distribution of each cell type can beconfirmed. Accordingly, the user can perform detailed determination forthe specimen, and, for example, can determine how much the cells of eachcell type can be included in the range where the distributions overlapeach other.

FIG. 26 shows another example of the analysis screen 900. Thescattergram 901, the enlargement button 903, the enlarged view 904, acell type selection region 906, and a three-dimensional histogram 907 asstatistic information are displayed on the analysis screen 900.

The cell type selection region 906 is provided with check boxes forselecting cell types. The cell type selection region 906 is providedwith a scroll bar for allowing display of all of the cell types bychanging the display range in the cell type selection region 906. Thethree-dimensional histogram 907 is frequency distribution informationobtained by subjecting all of the cells in the gate 901 a to countingfor each probability in a selected cell type. In the three-dimensionalhistogram 907, histograms for respective selected cell types arearranged in the front-rear direction so as to be displayed in athree-dimensional manner. In the example shown in FIG. 26, sincelymphocyte and monocyte have been selected in the cell type selectionregion 906, a histogram of lymphocyte and a histogram of monocyte aredisplayed together in the three-dimensional histogram 907.

According to the analysis screen 900 in FIG. 26, by selecting two ormore cell types and referring to the three-dimensional histogram 907,the user can simultaneously confirm the frequency distributions of thetwo or more cell types. In the example in FIG. 25, two histograms 905 aare simultaneously displayed in the screen. However, in the example inFIG. 26, when three or more cell types are selected, three or morehistograms are displayed in the screen. Thus, in the case of the screenin FIG. 26, when compared with the screen in FIG. 25, the user cansimultaneously understand a greater number of frequency distributions.In addition, the user can easily compare the frequencies for eachprobability.

The three-dimensional histogram 907 in FIG. 26 is a histogram in which aplurality of the histograms 905 a shown in FIG. 25 are arranged in thedepth direction. However, instead of this, distribution states ofprobabilities of a plurality of cell types may be displayed in terms ofsurface plot, or the like.

FIG. 27 shows another example of the analysis screen 900. Thescattergram 901, the enlargement button 903, the enlarged view 904, anda number display region 908 are displayed on the analysis screen 900 inFIG. 27.

In the number display region 908, with respect to all of the cells inthe gate 901 a, a count value obtained by counting, for each cell type,cells that have a probability higher than 0% is displayed in the form ofa bar graph as statistic information. In this case, a plurality of cellsin the gate 901 a are redundantly counted, and thus, the total of thecount values in the number display region 908 becomes greater than theactual number of cells in the gate 901 a. For example, it is assumedthat one cell that has probability information of monocyte 90%,neutrophil 5%, and immature granulocyte 5% is included in the gate 901 ain FIG. 27. In this case, this cell is counted as 1 for each ofmonocyte, neutrophil, and immature granulocyte. That is, when theprobability of belonging to a plurality of cell types is 0% or higher,the number of the cell is redundantly counted for the plurality of celltypes. The aggregation method is not limited thereto, and the number maybe expressed by a decimal not greater than 1 in accordance with theprobability. For example, as in the above example, when one cell thathas probability information of monocyte 90%, neutrophil 5%, and immaturegranulocyte 5% is included in the reference range of the gate 901 a,aggregation may be performed such that 0.9 monocyte, 0.05 neutrophil,and 0.05 immature granulocyte are considered to be included.

With respect to a bar graph in the number display region 908, when thecounting result of the cell type is smaller than a predeterminedthreshold (e.g., less than 10), the count value of the cell type isadded to the bar graph. In the example in FIG. 27, since the count valueof immature granulocytes is five, “5” is added to the bar graph ofimmature granulocyte. The number display region 908 is provided with ascroll bar for allowing display of all of the cell types by changing thedisplay range in the number display region 908.

According to the analysis screen 900 in FIG. 27, by referring to thenumber display region 908, the user can confirm the number of cells foreach cell type in the gate 901 a. Since a cell that has a probabilityexceeding a predetermined percentage is counted as one, thisconfiguration is convenient when confirming a majority cell type and aminority cell type. A cell type that has a small count value in thenumber display region 908 is provided with a count value as auxiliarydisplay so as to allow confirmation of the magnitude of the count value.Therefore, the count value of a cell type that has a small count valuecan be clearly confirmed. Accordingly, the user can smoothly determine apossibility that cells of a rare cell type can be included in thespecimen.

In the number display region 908, a count value obtained by countingcells of a cell type having a probability higher than 0% is displayed.However, the threshold for counting is not limited to 0%, and a valuegreater than 0% and smaller than 100% may be set. A user interface(e.g., a probability selection region 910 in FIG. 31) that can receive athreshold for counting may be provided in the screen.

FIG. 28 shows another example of the analysis screen 900. Thescattergram 901 and a detail display region 913 are displayed on theanalysis screen 900.

When the user has performed an operation of selecting a dot on thescattergram 901, statistic information about a cell corresponding to theselected dot is displayed in the detail display region 913. The detaildisplay region 913 has regions 913 a to 913 d. In the region 913 a, withrespect to one or a plurality of cells (hereinafter, “selected cell”)plotted at the selected dot, the cell types that have the highest twoprobabilities are displayed. In a region 913 b, with respect to theselected cell, the cell type that has the highest probability among thefive classifications of white blood cells is displayed. In a region 913c, with respect to the selected cell, the abnormal cell type that hasthe highest probability is displayed. In the region 913 d, with respectto the selected cell, cell types that may be present, i.e., cell typesthat have a probability higher than 0%, are displayed.

According to the analysis screen 900 in FIG. 28, by referring to thedetail display region 913, the user can understand into what cell typethe selected cell can be classified.

In the vicinities of the names of the cell types displayed in theregions 913 a to 913 d, the probabilities of the corresponding celltypes may be additionally displayed. When the number of selected cellsis one, the probability information of the corresponding cell can bedisplayed. When the number of selected cells is a plurality, arepresentative value of the probabilities of the cell types of theplurality of cells may be obtained and displayed. The representativevalue can be expressed in terms of the average value, the median, or themode, for example. In the region 913 a, only the cell type at thehighest rank may be displayed, or cell types at the highest three ormore ranks may be displayed. In the region 913 b, 913 c, cell types atthe highest two or more ranks may be displayed. In the region 913 d,cell types that have probabilities higher than a predetermined thresholdmay be displayed.

FIG. 29 is a flow chart showing a third example of the test resultdisplaying process. In step S414, the processor 3001 compares athreshold with the number of cells that correspond to the dot selectedon the scattergram 901. When the number of cells is not greater than thethreshold, the processor 3001 individually displays the classificationinformation for each cell in step S416. The individual display of theclassification information for each cell has been described withreference to FIG. 20 to FIG. 23. When the number of cells is greaterthan the threshold, the processor 3001 aggregates the classificationinformation and displays statistic information in step S415. The displayof statistic information has been described with reference to FIG. 25 toFIG. 28.

As described above, when a plurality of dots are selected at one time,or when a wide range for the gate 901 a is set, the number of cells tobe displayed may become too large. Thus, when the classificationinformation is individually displayed for each cell, the visibility forthe user may be impaired in some cases. Meanwhile, when the number ofcells corresponding to the selected dot is small, individual display ofthe classification information of each cell may be suitable for thepurpose of the user in some cases. For example, as in the case of thedot selected on the scattergram 901 in FIG. 28, a position away from thecenter of a cell cluster or a position where cell distribution is scarcehas been selected by the user, the user may desire to analyze cellsindividually, not as a population. Therefore, in the example in FIG. 29,when the number of selected dots is not greater than a predeterminedthreshold, pieces of classification information of cells areindividually displayed, and when the number of selected dots is greaterthan the predetermined threshold, the classification information is notindividually displayed but statistically displayed. Accordingly, ascreen having a high visibility for the user can be automaticallyprovided.

FIG. 30 shows a fourth example of the test result displaying process. Inthe first example to third example of the test result displaying processdescribed above, when a dot of a target cell is selected on thescattergram 901, the classification information of the cellcorresponding to the dot is displayed. In the examples in FIG. 31 toFIG. 38 described below, the analysis screen 900 that includes a graphis displayed (step S417), and dots of cells that satisfy a definedextraction condition are highlighted (step S418). In step S418,selection of a dot is not performed on the scattergram 901. Instead,when the user has defined a condition for a cell to be extracted, cellsthat satisfy the condition are extracted and displayed in the graph.

FIG. 31 shows another example of the analysis screen 900. Thescattergram 901, a cell type selection region 909, and a probabilityselection region 910 are displayed on the analysis screen 900.

The cell type selection region 909 and the probability selection region910 are each implemented as a so-called pull-down menu. The cell typeselection region 909 is configured such that one cell type can beselected from among all the cell types that correspond to themeasurement channel (in FIG. 31, the WDF channel) selected by the user.In the probability selection region 910, numerical values ofprobability, as choices, are displayed so as to be selectable at a 5%interval in a range of 0% to 100%. Specifically, the probabilityselection region 910 is configured such that a character string composedof an equal sign or an inequality sign and a probability, such as“=100%”, “>95%”, “>90%”, . . . , “>5%”, “>0%, or “=0%”, can be selected.

When the user has selected a target cell type in the cell type selectionregion 909 and has selected a probability in the probability selectionregion 910, a corresponding dot on the scattergram 901 is highlighted.In the example shown in FIG. 31, basophil has been selected as the celltype, and “>85%” has been selected as the probability. In this case,dots (dots in a region 901 b) of cells for which the probability ofbeing basophil is higher than 85% are highlighted on the scattergram901. The method of highlighting may be any display method that candistinguish the extracted dot from the other dots, such as displayingthe dot in a color different from the colors for the other dots,displaying the contour of the dot in an emphasized manner, or blinkingthe dot, for example.

According to the screen in FIG. 31, by defining the target cell type andprobability as the extraction condition, the user can understand theposition of the cell that matches the condition on the scattergram 901.Therefore, the user can understand which position on the conventionalscattergram 901 the cell classified according to the classificationinformation (cell type and probability) is present at. This informationcan be used as a material when the accuracy of classification accordingto the classification information is determined, for example.

FIG. 32 shows another example of the analysis screen 900. The analysisscreen 900 in FIG. 32 includes a scale bar 910 a for selecting anumerical value of probability and an extraction target selection region910 c, instead of the probability selection region 910. The scale bar910 a includes a pointer 910 b slidable in the left-right direction. Theuser can select a desired percentage in the range of 0% to 100% bymoving the pointer 910 b in the left-right direction. In the extractiontarget selection region 910 c, an inequality sign combined with thepercentage selected by the scale bar 910 a is displayed so as to beselectable. For example, as shown in FIG. 32, when basophil is selectedas the cell type in the cell type selection region 909, the pointer 910b is placed at the position of 80% in the scale bar 910 a, and “>” isselected in the extraction target selection region 910 c, the extractioncondition is set as “probability of basophil >80%”. In the scattergram901, dots of cells that satisfy the extraction condition of “probabilityof basophil >80%” are highlighted.

When the user moves the pointer 910 b in the scale bar 910 a, thenumerical range of percentage included in the extraction condition iscontinuously (stepwise) changed in association therewith. When theextraction condition has been changed, dots extracted on the scattergram901 are dynamically changed, accordingly.

For example, when the pointer 910 b is caused to slide rightward fromthe state shown in FIG. 32, the extraction condition is changed from“probability of basophil >80%” to “probability of basophil >85%”. Due tothis change in the extraction condition, dots of cells highlighted onthe scattergram 901 are also dynamically changed from thosecorresponding to “probability of basophil >80%” to those correspondingto “probability of basophil >85%”. That is, the number of dotshighlighted decreases. Conversely, when the pointer 910 b is caused toslide leftward, the extraction condition is changed from “probability ofbasophil >80%” to “probability of basophil >75%”. Due to this change inthe extraction condition, dots of cells highlighted on the scattergram901 are also dynamically changed from those corresponding to“probability of basophil >80%” to those corresponding to “probability ofbasophil >75%”. That is, the number of dots displayed in a highlightedmanner increases.

Thus, when the user causes the pointer 910 b of the scale bar 910 a toslide, thereby continuously changing the extraction condition, and, inassociation therewith, the user causes the highlight display of dots ofcells that match the extraction condition displayed on the scattergram901 to be dynamically changed, the user can easily understand at whichposition on the scattergram 901 the target cell is present.

FIG. 33 shows another example of the analysis screen 900. FIG. 33 showsanother example regarding setting of the extraction condition. Theanalysis screen 900 in FIG. 33 includes condition setting regions 910 d,910 e, 910 f for respectively setting an extraction condition 1, anextraction condition 2, and an extraction condition 3. The conditionsetting regions 910 d, 910 e, 910 f each include pull-down menus thatrespectively correspond to the cell type selection region 909 and theprobability selection region 910 shown in FIG. 31. Two operator settingregions 910 g, 910 h are provided between the condition setting regions910 d, 910 e, 910 f. The operator setting regions 910 g, 910 h are eachat the extraction conditions 1 to 3. Choices of the logical operator are“AND”, “OR”, “NOT”, and the like. In the example in FIG. 33, the logicaloperators are each “AND”.

When the extraction conditions 1 to 3 are defined in the respectivecondition setting regions 910 d, 910 e, 910 f, and logical operators areset in the operator setting regions 910 g, 910 h, a complex condition inwhich the extraction conditions 1 to 3 are combined by the logicaloperators is defined. In the scattergram 901, dots (dots in the region901 b) of cells that satisfy the defined complex condition arehighlighted. As in the example in FIG. 33, since cells are extracted notusing a single extraction condition obtained as a one-to-one combinationof cell type and probability, but using a complex condition obtainedthrough a combination of a plurality of extraction conditions, moredetailed extraction condition setting can be realized.

FIG. 34 shows another example of the analysis screen 900. Thescattergram 901, a first histogram 910 i, a second histogram 910 j, anda display region 910 k are displayed on the screen in FIG. 34.

In the example shown in FIG. 34, only the scattergram 901 is displayedas default on the analysis screen 900. The user can select a specifictype of cell population on the displayed scattergram 901. For example,when the user has placed a cursor on a specific cell population on thescattergram 901, the cell population is selected.

FIG. 34 shows a state where a population of lymphocytes has beenselected. When a specific type of cell population has been selected, thefirst histogram 910 i showing the probability distribution of theselected cell population, and the second histogram 910 j showing theprobability distribution regarding abnormal cells that correspond to theselected cell population are displayed on the analysis screen 900. Thefirst histogram 910 i and the second histogram 910 j are each similar tothe histogram 905 a in FIG. 25, for example, and indicates the number ofcells with respect to the axis representing the probability. Abnormalcells corresponding to the selected cell population mean abnormal cellsthat are morphologically similar to the selected cell population. Theabnormal cells are determined on the basis of a correspondencerelationship determined in advance, in accordance with the selected cellpopulation. For example, when lymphocyte has been selected, the abnormalcells can be abnormal lymphocytes or blasts. For example, when monocytehas been selected, the abnormal cells can be blasts. For example, wheneosinophil or basophil has been selected, the abnormal cells can beimmature granulocytes.

In the example in FIG. 34, when a specific cell population has beenselected, cells that have probabilities of being abnormal cellscorresponding to the selected cell population are extracted, and thedistribution of the probabilities is displayed as the second histogram910 j. When the user compares the probability distribution of theselected cell population with the probability distribution of abnormalcells, the user can understand how close to 100% the probabilitydistribution of the selected cell population is. Conversely, the usercan statistically understand how many cells that have probabilities ofbeing abnormal cells are included in the selected cell population.

The analysis screen 900 in FIG. 34 includes the display region 910 kthat displays an indicator based on statistic information, in additionto the first histogram 910 i and the second histogram 910 j. In thedisplay region 910 k, “In the lymphocyte population, the proportion ofcells that have probabilities of being abnormal lymphocytes is 15%(previous value: 12%).” is displayed, for example. That is, theproportion of cells that have the selected cell population as the parentpopulation and that have probabilities of being corresponding abnormalcells is displayed as an indicator in the display region 910 k.

In the example in FIG. 34, the sample with respect to the parentpopulation is defined as “cells that have probabilities of beingabnormal cells”. However, the definition of the sample is not limitedthereto. For example, the sample may be defined as “cells that are inthe second highest rank in which the cells have probabilities of beingabnormal cells. In this case, only cells that have higher probabilitiesof being abnormal cells can be extracted so as to calculate anindicator, which may lead to reduction of noise. The indicator is notlimited to a quantitative indicator such as a proportion, and may be aqualitative indicator that indicates the level stepwise in accordancewith the absolute number of cells that have probabilities of beingabnormal cells.

In the example in FIG. 34, the number or proportion of cells that haveprobabilities of being abnormal cells can be easily understood withrespect to a specific cell population. The configurations in FIG. 25 toFIG. 28 described above are advantageous in that the user can designatea specific cell or cell population on the scattergram 901 as desired.However, in those configurations, it may be difficult for the user todesignate only a specific cell population on the scattergram 901. When acell present at the circumference of a cluster is also at an adjacentcluster or is separated from a cluster, such designation may beespecially difficult. In the example in FIG. 34, simply by placing acursor on a specific cell population on the scattergram 901, e.g.,simply by placing a cursor on a population of lymphocytes, a cellpopulation that has lymphocytes as main cells can be selected. Inaddition, cells that have probabilities of being abnormal lymphocytes,which are abnormal cells that correspond to lymphocytes, areautomatically extracted, and a probability distribution and an indicatorare displayed. Therefore, the user can easily recognize how many cellsthat have probabilities of being abnormal cells are included althoughthe main cells are normal cells.

FIG. 35 shows another example of the analysis screen 900. Thescattergram 901, the cell type selection region 909, and a histogram 911are displayed on the screen in FIG. 35.

FIG. 35 shows an example in which the WPC channel has been designated asthe measurement channel to be displayed. Therefore, the scattergram 901is displayed on the basis of the feature parameters of all of the cellsthat correspond to the measurement channel, and the cell type selectionregion 909 is configured such that, as the cell types to be displayed,all cell types, such as mature white blood cell, abnormal lymphocyte,and blast, that are associated with the WPC channel can be selected.

When the user has performed an operation of selecting a cell type in thecell type selection region 909, the histogram 911 is displayed on thebasis of the selected cell type. The histogram 911 is frequencydistribution information obtained by counting cells of the selected celltype for each probability. In the histogram 911, the horizontal axisrepresents probability and the vertical axis represents the number ofcells. When the user has performed an operation of selecting a bar graphon the histogram 911, corresponding dots (dots in a region 901 c) on thescattergram 901 are highlighted. In the example shown in FIG. 35,abnormal lymphocyte has been selected as the cell type, and the bargraph of a probability of 70% has been selected. In this case, dots ofcells (cells distributed near the region 901 c) for which probabilitiesof being abnormal lymphocytes are on the order of 70% are highlighted onthe scattergram 901.

According to the analysis screen 900 in FIG. 35, similar to the analysisscreens 900 in FIG. 31 to FIG. 33, when the user selects a cell type anda probability, the user can understand the position of each cell thatmatches the condition on the scattergram 901. Therefore, the user canunderstand at which position on the conventional scattergram 901 thecell classified according to the classification information (cell typeand probability) is present.

In the examples in FIG. 31 to FIG. 35 described above, dots of cellsthat satisfy an extraction condition composed of a combination of a celltype and a probability are displayed on the scattergram 901. Instead ofthe scattergram 901, the number of cells that satisfy the extractioncondition may be displayed in terms of a histogram as shown in FIG. 36to FIG. 38.

The probability selection region 910 and a number display region 912 aredisplayed on the analysis screens 900 in FIG. 36 to FIG. 38.

On the analysis screen 900 in FIG. 36, when the user has performed anoperation of selecting a probability in the probability selection region910, the number of cells corresponding to the probability selected inthe probability selection region 910, among all of the cellscorresponding to the target measurement channel, is displayed for eachcell type in the number display region 912. The number display region912 is provided with a descending order button 912 a for performingsetting such that cell types are arranged downwardly staring from thecell type having the greatest number; and an ascending order button 912b for performing setting such that cell types are arranged downwardlystarting from the cell type having the smallest number. FIG. 36 shows astate where the descending order button 912 a has been operated. FIG. 37shows a state where the ascending order button 912 b has been operated.FIG. 38 shows a state where, on the screen in FIG. 37, “>20%” has beenset as a probability condition.

According to the screens in FIG. 36 to FIG. 38, after the user hasselected a probability in the probability selection region 910, the usercan, by operating the descending order button 912 a, set display of thenumber display region 912 such that the numbers are arranged in adescending order as shown in FIG. 36, and the user can, by operating theascending order button 912 b, set display of the number display region912 such that the numbers are arranged in an ascending order as shown inFIG. 37. Accordingly, among the cell types corresponding to theprobability of the selected condition, the user can smoothly confirm acell type of which the number of cells is large or a cell type of whichthe numbers of cells is small, for example.

When the user selects again another probability in the probabilityselection region 910 in the state shown in FIG. 37, the user can resetthe display of the number display region 912 in accordance with thereselected probability as shown in FIG. 38. Thus, by operating theprobability selection region 910, it is possible to smoothly confirm thenumber of cells of each cell type based on a different probabilitycondition.

The scattergram 901 may be provided also in FIG. 36 to FIG. 38. In thiscase, when a bar graph in the number display region 912 has beenselected, cells (dots) corresponding to the selected bar graph may behighlighted in the scattergram 901. In addition, the histogram 911 (seeFIG. 35) may be provided also in FIG. 36 to FIG. 38. In this case, whena bar graph in the number display region 912 has been selected, thestate of distribution of the probabilities regarding the cell type ofthe selected bar graph may be displayed on the histogram 911.

FIGS. 39A, 39B each schematically show another configuration of thescattergram 901 described above.

In the scattergram 901 shown in FIG. 39A, distribution information ofcells according to the probabilities for each cell type is superimposedon the plots of cells according to the feature parameters (in FIG. 39A,SSCP, SFLP). The distribution information in this case is composed of aring pattern in which cells having the same probability are connected ina contour line shape; and a distribution region in which ranges eachhaving similar probabilities are expressed with different darkness. InFIG. 39A, the higher the probability is, the darker color thedistribution region has. In FIG. 39A, cells (dots) based on the featureparameters are plotted on the scattergram 901. However, as shown in FIG.39B, dots of cells based on the feature parameters may not necessarilybe plotted on the scattergram 901.

In the scattergrams 901 shown in FIGS. 39A, 39B, distributioninformation of cells according to the probabilities for each cell typeis also displayed in the conventional scattergram 901 based on thefeature parameters. Accordingly, with reference to the conventionalscattergram 901, the user can visually understand how the probabilitiesof each cell type are distributed.

FIG. 40 is a flow chart of a fifth example of the test result displayingprocess. In the first to fourth examples described above, the analysisscreen 900 is displayed when a scattergram on the detail screen 800 hasbeen double clicked. In the fifth example, when a “research” tab hasbeen selected on the detail screen 800 in FIG. 19, a research screen900R is displayed instead of the analysis screen 900 (step S419). Inthis example, an analysis result based on a main cell type is displayedon the specimen list screen 700 and the detail screen 800, and ananalysis result screen or information regarding cell types other thanthe main cell type is displayed only on the research screen 900R. Then,on the basis of a research flag, classification information of theselected cell type is read out, and a graph is displayed (step S420).

The screen in FIG. 41 is an example of a screen that is displayed when aresearch information button 921 has been operated. A cell type selectionregion 933 and a number display region 934 are displayed on the screenin FIG. 41. The cell type selection region 933 includes check boxes eachfor allowing selection of a cell type regarding the designatedmeasurement channel.

When the user has performed an operation of selecting cell types in thecell type selection region 933, a count value obtained by counting, withrespect to the selected cell types, cells of each cell type that has aprobability higher than 0% is displayed, for each cell type, in the formof a bar graph in the number display region 934.

Thus, when the user has operated the research information button 921,and then selected a cell type in the cell type selection region 933 onthe screen in FIG. 41, the user can confirm a detailed analysis resultregarding minor cells, with reference to the number display region 934.That is, as shown in FIG. 41, in a research information display mode,the numbers of blasts and abnormal lymphocytes that cannot usually bemain cells are also displayed. Therefore, the user can perform a furtherdetailed analysis on the basis of the analysis result displayed in theresearch information display mode.

The screen shown in FIG. 42 is an example of a screen that is displayedwhen the research information button 921 has been operated. The celltype selection region 933, a histogram 935, and a scattergram 936 aredisplayed on the screen in FIG. 42.

When the user has performed an operation of selecting a cell type in thecell type selection region 933, the histogram 935 is displayed on thebasis of the selected cell type. The histogram 935 is frequencydistribution information obtained by counting, for each probability ofthe selected cell type, all the cells that correspond to the measurementchannel. When the user has selected a bar graph in the histogram 935,corresponding cells (dots) (dots in a region 936 a) on the scattergram936 are highlighted. In the example shown in FIG. 42, blast has beenselected as the cell type, and a bar graph of a probability of 50% hasbeen selected. In this case, cells (cells distributed near the region936 a) for which probabilities of being blasts are on the order of 50%are highlighted on the scattergram 936.

Thus, when the user has operated the research information button 921,then, on the screen in FIG. 42, selected a cell type in the cell typeselection region 933 and selected a bar graph on the histogram 935, theuser can confirm a further detailed analysis result.

When a plurality of cell types have been selected in the cell typeselection region 933 in FIG. 42, graphs of the plurality of cell typesare displayed in the histogram 935, as in the three-dimensionalhistogram 907 in FIG. 26. When a plurality of bar graphs have beenselected on the histogram 935, all of the corresponding cells (dots) onthe scattergram 936 are highlighted.

7. Host Computer Transmission Process

Next, data (hereinafter, referred to as “output data”) to be sent fromthe processing unit 300 to the host computer 500 is described. FIG. 43is a flow chart showing a subroutine of host computer transmission.

The processor 3001 of the processing unit 300 receives a validationoperation from the user and generates output data to be transmitted tothe host computer 500 in accordance with the validation operation (stepS141).

As described with reference to FIG. 18 and FIG. 19, on the specimen listscreen 700 and the detail screen 800 which are each a test resultscreen, the validation icon 710 d displayed in common between thesescreens is displayed. The user confirms the test result screen describedabove, and performs verification on the test result. As a result of theverification, when the user has determined that the result can bereported to the host computer 500, the user clicks the validation icon710 d. When the user does not execute validation on the target specimenand sets a retest for the target subject, the user operates themeasurement registration icon 710 c to create a test order for theretest.

When a validation operation has been performed, the processor 3001generates output data to be transmitted to the host computer 500, on thebasis of analysis result data stored in the storage 3004 (step S141).Then, the processor 3001 transmits the generated output data to the hostcomputer 500 (step S142).

FIG. 44A to FIG. 45B each schematically show a configuration of outputdata that is generated by the processor 3001 and that is to betransmitted to the host computer 500. The output data may be formed byremoving classification information that corresponds to a certain celltype from the test result data, and is configured as shown in one ofthose shown in FIG. 44A to FIG. 45B, for example.

The output data shown in FIG. 44A is composed of: specimen ID;measurement result; measurement channel; cell ID; identificationinformation regarding a main cell type; and probability regarding themain cell type. That is, the output data shown in FIG. 44A is obtainedby removing data other than the output data shown in FIG. 44A from thetest result data shown in FIGS. 12 to 16.

The output data shown in FIG. 44B is composed of: specimen ID;measurement result; measurement channel; cell ID; identificationinformation regarding cell types that have the highest twoprobabilities; and probabilities regarding the cell types that have thehighest two probabilities. That is, the output data shown in FIG. 44B isobtained by adding, to the output data shown in FIG. 44A, theidentification information and probability regarding the cell type thathas the second highest probability. The output data shown in FIG. 44B isnot limited to having the highest two cell types and probabilities, andmay have cell types and probabilities from the highest rank to apredetermined rank.

The output data shown in FIG. 45A is composed of specimen ID andmeasurement result. That is, the output data shown in FIG. 45A isobtained by removing data other than the output data shown in FIG. 45Afrom the test result data shown in FIGS. 12 to 16. That is, the outputdata does not include information regarding probability at all.

The output data shown in FIG. 45B is composed of specimen ID,measurement result, and abnormal cell detection information. That is,the output data shown in FIG. 45B is obtained by adding abnormal celldetection information to the measurement result in the output datasimilar to that in FIG. 45A. When not less than a predetermined numberof abnormal cells (e.g., abnormal lymphocytes or blasts) that have aprobability higher than 0% are present in the specimen, a fact that theabnormal cells have been detected is added as abnormal cell detectioninformation to the measurement result at the time of generation of thetest result data. The abnormal cell detection information is a characterstring such as “abnormal lymphocytes have been detected”, “Abn LYMPH?”,“blasts have been detected”, or “Blast?”, for example. The abnormal celldetection information may be the cell type of the detected abnormalcells and the number thereof. The abnormal cell detection information isdisplayed in the measurement result display region 932 in FIG. 18, forexample.

As described above, output data that includes a counting result and inwhich at least a part of relevance information (such as cell type andprobability) has been removed is transmitted to the host computer 500.As described above, when probabilities associated with a plurality ofcell types are obtained for each cell, the volume of test result databecomes huge. Therefore, if all of the test result data is transmittedto the host computer 500, the communication load due to datatransmission becomes large. In contrast to this, according to the outputdata shown in FIG. 44A to FIG. 45B, not all the huge test result data istransmitted, but only data that is necessary for management by the hostcomputer 500 is transmitted. Accordingly, both of appropriate datamanagement in the host computer 500 and efficient data transmission canbe realized.

The output data shown in FIG. 44A to FIG. 45B may include featureparameters and scattergrams.

Here, the processor 3001 generates the output data so as to have aconfiguration of one of those shown in FIG. 44A to FIG. 45B. However,the configuration of the output data may be set in accordance with aninstruction from a user.

FIG. 46A and FIG. 46B each schematically show a configuration of ascreen for selecting data to be included in the output data.

A cell type selection region 951 and an OK button 952 are displayed onthe screen in FIG. 46A. The cell type selection region 951 is providedwith check boxes each for selecting a cell type. When the user hasperformed an operation of selecting a cell type in the cell typeselection region 951 and has operated the OK button 952, the processor3001 of the processing unit 300 stores the set content into the storage3004. From the next time, when generating output data, the processor3001 causes the set cell type and the probability corresponding to theset cell type to be included in the output data.

A cell range selection region 961 and an OK button 962 are displayed onthe screen in FIG. 46B. The cell range selection region 961 is providedwith check boxes each for selecting a cell type range. A cell typerange, such as “main cell type only”, “minor cell types other than maincell type”, “all cell types”, or “do not transmit”, can be selected inthe cell range selection region 961. When “main cell type only” has beenselected, only the identification information and probability of themain cell type among all of the identification information andprobabilities are caused to be included in the output data. When “minorcell types other than main cell type” has been selected, only theidentification information and probabilities of all of the minor celltypes other than the main cell type among all of the identificationinformation and probabilities are caused to be included in the outputdata. When “all cell types” has been selected, all of the identificationinformation and probabilities are caused to be included in the outputdata. When “do not transmit” has been selected, none of theidentification information and probabilities are caused to be includedin the output data.

When the user has performed an operation of selecting a cell type rangein the cell range selection region 961 and has operated the OK button962, the processor 3001 stores the set content into the storage 3004.From the next time, when generating output data, the processor 3001generates output data on the basis of the set cell type range.

8. Configurations of Processor and Parallel-Processing ProcessorConfiguration Example 1

Next, a configuration of the parallel-processing processor 4833installed in the measurement unit 400 is described in detail withreference FIG. 2 again. In the following, configuration example 1 isdescribed with respect to the processor 4831 and the parallel-processingprocessor 4833.

As described with reference to FIG. 2, using the parallel-processingprocessor 4833, the processor 4831 executes an analysis process ofwaveform data included in a generated digital signal, in accordance withthe deep learning algorithm 60. That is, the processor 4831 isprogrammed to execute an analysis process of waveform data included in adigital signal in accordance with the deep learning algorithm 60. Theanalysis software 4835 a for analyzing data of cells on the basis of thedeep learning algorithm 60 may be stored in the storage 4835. In thiscase, the processor 4831 executes the analysis software 4835 a stored inthe storage 4835, thereby executing a data analysis process based on thedeep learning algorithm 60. The processor 4831 is a CPU (CentralProcessing Unit), for example. For example, core i9, Core i7, or Core i5manufactured by Intel Corporation, Ryzen 9, Ryzen 7, Ryzen 5, or Ryzen 3manufactured by AMD, or the like may be used as the processor 4831.

The processor 4831 controls the parallel-processing processor 4833. Theparallel-processing processor 4833 executes parallel processingregarding a matrix operation, for example, in accordance with control bythe processor 4831. That is, the processor 4831 is a master processor ofthe parallel-processing processor 4833, and the parallel-processingprocessor 4833 is a slave processor of the processor 4831. The processor4831 is also referred to as a host processor or a main processor.

The parallel-processing processor 4833 executes in parallel a pluralityof arithmetic processes being at least a part of processing regardinganalysis of waveform data. The parallel-processing processor 4833 is aGPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array),or an ASIC (Application Specific Integrated Circuit), for example. Whenthe parallel-processing processor 4833 is an FPGA, theparallel-processing processor 4833 may have programed therein in advancean arithmetic process regarding a trained deep learning algorithm 60,for example. When the parallel-processing processor 4833 is an ASIC, theparallel-processing processor 4833 may have incorporated therein inadvance a circuit for executing the arithmetic process regarding thetrained deep learning algorithm 60, or may have a programmable modulebuilt therein in addition to such an incorporated circuit, for example.As the parallel-processing processor 4833, GeForce, Quadro, TITAN,Jetson, or the like of NVIDIA Corporation may be used, for example. Inthe case of a Jetson series, Jetson Nano, Jetson Tx2, Jetson Xavier, orJetson AGX Xavier is used, for example.

The processor 4831 executes a calculation process regarding control ofthe measurement unit 400, for example. The processor 4831 executes acalculation process regarding control signals transmitted/receivedbetween the apparatus mechanism part 430, the sample preparation part440, and the specimen suction part 450. The processor 4831 executes acalculation process regarding transmission/reception of information withrespect to the processing unit 300, for example. The processor 4831executes processes regarding reading out of program data from thestorage 4835, developing a program onto the RAM 4834, andtransmission/reception of data with respect to the RAM 4834. Theprocesses described above and executed by the processor 4831 arerequired to be executed in a predetermined order, for example. Forexample, when processes needed for control of the apparatus mechanismpart 430, the sample preparation part 440, and the specimen suction part450 are assumed to be A, B, and C, the processes are required to beexecuted in the order of B, A, and C in some cases. Since the processor4831 often executes such continuous processes that depend on an order,even when the number of arithmetic units (each may be referred to as a“processor core” a “core”, or the like) is increased, the processingspeed is not necessarily increased.

Meanwhile, the parallel-processing processor 4833 executes a largeamount of regular calculation processes such as arithmetic operations onmatrix data including a large amount of elements, for example. In thepresent embodiment, the parallel-processing processor 4833 executesparallel processing in which at least a part of processes of analyzingwaveform data in accordance with the deep learning algorithm 60 areparallelized. The deep learning algorithm 60 includes a large amount ofmatrix operations, for example. For example, the deep learning algorithm60 may include at least 100 matrix operations, or may include at least1000 matrix operations. The parallel-processing processor 4833 has aplurality of arithmetic units, and the respective arithmetic units cansimultaneously execute matrix operations. That is, theparallel-processing processor 4833 can execute, in parallel, matrixoperations by a plurality of respective arithmetic units, as parallelprocessing. For example, a matrix operation included in the deeplearning algorithm 60 can be divided into a plurality of arithmeticprocesses that are not order-dependent with each other. The thus dividedarithmetic processes can be executed in parallel by a plurality ofarithmetic units, respectively. These arithmetic units may be eachreferred to as a “processor core”, a “core”, or the like.

As a result of execution of such parallel processing, speed up ofarithmetic processing in the entirety of the measurement unit 400 can berealized. A process such as a matrix operation included in the deeplearning algorithm 60 may be referred to as “Single Instruction MultipleData (SIMD) processing”, for example. The parallel-processing processor4833 is suitable for such an SIMD operation, for example. Such aparallel-processing processor 4833 may be referred to as a vectorprocessor.

As described above, the processor 4831 is suitable for executing diverseand complicated processes. Meanwhile, the parallel-processing processor4833 is suitable for executing in parallel a large amount of regularprocesses. Through parallel execution of a large amount of regularprocesses, the TAT (Turn Around Time) required for the calculationprocesses is shortened.

The parallel processing to be executed by the parallel-processingprocessor 4833 is not limited to matrix operations. For example, whenthe parallel-processing processor 4833 executes a learning process inaccordance with the deep learning algorithm 60, differential operationsor the like regarding the learning process can be the target of theparallel processing.

As for the number of arithmetic units of the processor 4831, a dual core(the number of cores: 2), a quad core (the number of cores: 4), or anocta core (the number of cores: 8) is adopted, for example. Meanwhile,the parallel-processing processor 4833 has at least ten arithmetic units(the number of cores: 10), and can execute ten matrix operations inparallel, for example. The parallel-processing processor 4833 that hasseveral-ten arithmetic units also exists. The parallel-processingprocessor 4833 that has, for example, at least 100 arithmetic units (thenumber of cores: 100) and that can execute 100 matrix operations inparallel also exists. The parallel-processing processor 4833 that has,for example, several-hundred arithmetic units also exists. Theparallel-processing processor 4833 that has, for example, at least 1000arithmetic units (the number of cores: 1000) and that can execute 1000matrix operations in parallel also exists. The parallel-processingprocessor 4833 that has, for example, several thousand arithmetic unitsalso exists.

FIG. 47 shows a configuration example of the parallel-processingprocessor 4833. The parallel-processing processor 4833 includes aplurality of arithmetic units 4836 and a RAM 4837. The respectivearithmetic units 4836 execute arithmetic processes of matrix data inparallel. The RAM 4837 stores data regarding the arithmetic processesexecuted by the arithmetic units 4836. The RAM 4837 is a memory that hasa capacity of at least 1 gigabyte. The RAM 4837 may be a memory that hasa capacity of 2 gigabytes, 4 gigabytes, 6 gigabytes, 8 gigabytes, 10gigabytes, or more. Each arithmetic unit 4836 obtains data from the RAM4837 and executes an arithmetic process. The arithmetic unit 4836 may bereferred to as a “processor core”, “core”, or the like.

FIG. 48 to FIG. 50 each show an installation example of theparallel-processing processor 4833 to the measurement unit 400. FIG. 48to FIG. 50 each show an example in which the parallel-processingprocessor 4833 is installed, in the cell analyzer 100, in the form ofbeing incorporated in the measurement unit 400. FIG. 48 and FIG. 49 eachshow an installation example in which the processor 4831 and theparallel-processing processor 4833 are provided as separate bodies. Asshown in FIG. 48, the processor 4831 is installed on a substrate 4838,for example. The parallel-processing processor 4833 is installed on agraphic board 4830, and the graphic board 4830 is connected to thesubstrate 4838 via a connector 4839, for example. The processor 4831 isconnected to the parallel-processing processor 4833 via a bus 485. Asshown in FIG. 49, the parallel-processing processor 4833 may be directlyinstalled on the substrate 4838, and connected to the processor 4831 viathe bus 485, for example. FIG. 50 shows an installation example in whichthe processor 4831 and the parallel-processing processor 4833 areintegrally provided. As shown in FIG. 50, the parallel-processingprocessor 4833 may be built in the processor 4831 installed on thesubstrate 4838, for example.

FIG. 51 shows another installation example of the parallel-processingprocessor 4833 to the measurement unit 400. FIG. 51 shows an example inwhich the parallel-processing processor 4833 is installed to themeasurement unit 400 by means of an external apparatus 4800 connected tothe measurement unit 400. For example, the parallel-processing processor4833 is mounted on the external apparatus 4800 being a USB (UniversalSerial Bus) device, and this USB device is connected to the bus 485 viaan interface part 487, whereby the parallel-processing processor 4833 isinstalled to the cell analyzer 100. The USB device may be a small devicesuch as a USB dongle, for example. The interface part 487 is a USBinterface having a transfer rate of several hundred Mbps, for example,and more preferably, is a USB interface having a transfer rate ofseveral Gbps to several ten Gbps or higher. As the external apparatus4800 having the parallel-processing processor 4833 mounted thereon,Neural Compute Stick 2 manufactured by Intel Corporation may be used,for example.

A plurality of USB devices each having the parallel-processing processor4833 mounted thereon may be connected to the interface part 487, wherebya plurality of parallel-processing processors 4833 may be installed tothe cell analyzer 100. The parallel-processing processor 4833 mounted onone USB device may have a smaller number of arithmetic units 4836 than aGPU or the like in some cases. Therefore, when a plurality of USBdevices are connected to the measurement unit 400, scale-up of thenumber of cores can be realized.

FIG. 52, FIG. 53A, FIG. 53B, and FIG. 54 each show an outline ofarithmetic processes executed by the parallel-processing processor 4833on the basis of control of the analysis software 4835 a which operateson the processor 4831.

FIG. 52 shows a configuration example of the parallel-processingprocessor 4833 which executes arithmetic processes.

The parallel-processing processor 4833 includes a plurality of thearithmetic units 4836 and the RAM 4837. The processor 4831, whichexecutes the analysis software 4835 a, can issue an order to theparallel-processing processor 4833 to cause the parallel-processingprocessor 4833 to execute at least a part of arithmetic processesnecessary for analysis of waveform data performed by the deep learningalgorithm 60. The processor 4831 orders the parallel-processingprocessor 4833 to execute arithmetic processes regarding waveform dataanalysis based on the deep learning algorithm. All or at least a part ofwaveform data corresponding to the signals detected by the FCM detectionpart 410 is stored in the RAM 4834. The data stored in the RAM 4834 istransferred to the RAM 4837 of the parallel-processing processor 4833.The data stored in the RAM 4834 is transferred to the RAM 4837 by a DMA(Direct Memory Access) method, for example. The plurality of therespective arithmetic units 4836 of the parallel-processing processor4833 execute in parallel arithmetic processes with respect to the datastored in the RAM 4837. Each of the plurality of the arithmetic units4836 obtains necessary data from the RAM 4837, to execute an arithmeticprocess. Data corresponding to the arithmetic result is stored into theRAM 4837 of the parallel-processing processor 4833. The datacorresponding to the arithmetic result is transferred from the RAM 4837to the RAM 4834 by a DMA method, for example.

FIG. 53A and FIG. 53B each show an outline of a matrix operationexecuted by the parallel-processing processor 4833.

Prior to analyzing waveform data in accordance with the deep learningalgorithm 60, calculation of the product of a matrix (matrix operation)is executed. The parallel-processing processor 4833 executes in parallela plurality of arithmetic processes regarding the matrix operation, forexample. FIG. 53A shows a calculation formula of the product of amatrix. In the calculation formula shown in FIG. 53A, a matrix c isobtained by a product of a matrix a of n rows×n columns and a matrix bof n rows×n columns. As shown as an example in FIG. 53A, the calculationformula is described in a hierarchical loop syntax. FIG. 53B shows anexample of arithmetic processes executed in parallel in theparallel-processing processor 4833. The calculation formula shown as anexample in FIG. 53A can be divided into n×n arithmetic processes, n×nbeing the number of combinations of a loop variable i for the firsthierarchical level and a loop variable j for the second hierarchicallevel, for example. Such divided arithmetic processes are arithmeticprocesses that are not dependent on each other, and thus can be executedin parallel.

FIG. 54 is a conceptual diagram showing that a plurality of arithmeticprocesses shown as an example in FIG. 53B are executed in parallel inthe parallel-processing processor 4833.

As shown in FIG. 54, each of the plurality of arithmetic processes isassigned to one of the plurality of the arithmetic units 4836 of theparallel-processing processor 4833. The respective arithmetic units 4836execute in parallel the assigned arithmetic processes. That is, therespective arithmetic units 4836 simultaneously execute the dividedarithmetic processes.

Through the arithmetic operations shown as an example in FIG. 53A, FIG.53B, and FIG. 54 and performed by the parallel-processing processor4833, information regarding a probability that a cell corresponding tothe waveform data belongs to each of a plurality of cell types isobtained. On the basis of the results of the arithmetic operations, theprocessor 4831, which executes the analysis software 4835 a, performsanalysis regarding the cell types of the cell that corresponds to thewaveform data. The arithmetic results are stored in the RAM 4837 of theparallel-processing processor 4833, to be transferred from the RAM 4837to the RAM 4834. The processor 4831 transmits a result of analysisperformed on the basis of the arithmetic results stored in the RAM 4834,to the processing unit 300 via the bus 485 and the interface part 489.

The arithmetic operations of the probability that a cell belongs to eachof a plurality of cell types may be performed by a processor differentfrom the parallel-processing processor 4833. For example, the arithmeticresults by the parallel-processing processor 4833 may be transferredfrom the RAM 4837 to the RAM 4834, and, on the basis of the arithmeticresults read out from the RAM 4834, the processor 4831 may performarithmetic operations on the information regarding the probability thata cell corresponding to each piece of waveform data belongs to each of aplurality of cell types. Alternatively, the arithmetic results by theparallel-processing processor 4833 may be transferred from the RAM 4837to the processing unit 300, and a processor installed in the processingunit 300 may perform arithmetic operations on the information regardingthe probability that a cell corresponding to each piece of waveform databelongs to each of a plurality of cell types.

In the present embodiment, the processes shown in FIG. 53A, FIG. 53B,and FIG. 54 are applied to an arithmetic process (also referred to as afiltering process) regarding a convolution layer in the deep learningalgorithm 60, for example.

FIG. 55A and FIG. 55B each show an outline of an arithmetic processregarding a convolution layer.

FIG. 55A shows an example of waveform data of forward scattered light(FSC), as waveform data to be inputted to the deep learning algorithm60. As described with reference to FIG. 5, the waveform data of thepresent embodiment is one-dimensional matrix data. To put it moresimply, the waveform data is a sequence in which elements are arrangedin a line. Here, for convenience of description, the number of elementsof the waveform data is assumed to be n (n is an integer 1 or greater).FIG. 55A shows a plurality of filters. Each filter is generated througha learning process of the deep learning algorithm 60. Each of theplurality of filters is one-dimensional matrix data indicating featuresof the waveform data. Although each filter shown in FIG. 55A is matrixdata of 1 row×3 columns, the number of columns is not limited to three.A matrix operation is performed on the waveform data inputted to thedeep learning algorithm 60 and each filter, whereby featurescorresponding to the cell type of the waveform data are calculated. FIG.55B shows an outline of a matrix operation performed on waveform dataand a filter. As shown in FIG. 55B, a matrix operation is executed whileeach filter is shifted with respect to the elements of the waveformdata, one by one. Calculation of the matrix operation is executedaccording to Formula 1 below.

$\begin{matrix}\left\lbrack {{Math}1} \right\rbrack &  \\{\underset{P = 0}{\sum\limits^{L - 1}}{\underset{q = 0}{\sum\limits^{M - 1}}{h_{pq}x_{{i + p},{j + q}}}}} & \left( {{formula}1} \right)\end{matrix}$

In Formula 1, the suffixes of x are variables that indicate the rownumber and the column number of the waveform data. The suffixes of h arevariables that indicate the row number and the column number of thefilter. In the example shown in FIGS. 55A, 55B, the waveform data isone-dimensional matrix data and the filter is matrix data of 1 row×3columns, and thus, L=1, M=3, p=0, q=0, 1, 2, i=0, j=0, 1, . . . , n−1.

The parallel-processing processor 4833 executes in parallel the matrixoperation represented by Formula 1, by means of the plurality of therespective arithmetic units 4836. On the basis of the arithmeticprocesses executed by the parallel-processing processor 4833,classification information regarding cell types of each cell isgenerated. The generated classification information is transmitted tothe processing unit 300 which is used in generation and display of atest result of the specimen based on the classification information.

Next, with reference to FIG. 56, a process of cell classification instep S13 in the flow chart of FIG. 10 is described.

The process of cell classification in step S13 is a process performed bythe processor 4831 in accordance with operation of the analysis software4835 a. The processor 4831 causes digital signals taken into the RAM4834 in step S13 to be transferred to the parallel-processing processor4833 (step S101). As shown in FIG. 52, the processor 4831 causes eachdigital signal to be transferred from the RAM 4834 to the RAM 4837through DMA transfer. The processor 4831 controls the bus controller4850, for example, to cause the digital signal from the RAM 4834 to beDMA-transferred to the RAM 4837.

The processor 4831 instructs the parallel-processing processor 4833 toexecute parallel processing onto the waveform data included in thedigital signal (step S102). The processor 4831 instructs execution ofthe parallel processing by calling a kernel function of theparallel-processing processor 4833, for example. The process executed bythe parallel-processing processor 4833 will be described later withreference to a flow chart shown as an example in FIG. 57. The processor4831 instructs the parallel-processing processor 4833 to execute amatrix operation regarding the deep learning algorithm 60, for example.The digital signal is decomposed into a plurality of pieces of waveformdata, to be sequentially inputted to the deep learning algorithm 60. Anindex corresponding to each cell and included in the digital signal isnot inputted to the deep learning algorithm 60. The waveform datainputted to the deep learning algorithm 60 is subjected to arithmeticoperations performed by the parallel-processing processor 4833.

The processor 4831 receives arithmetic results obtained throughexecution by the parallel-processing processor 4833 (step S103). Thearithmetic results are DMA-transferred from the RAM 4837 to the RAM 4834as shown in FIG. 52, for example.

On the basis of the arithmetic results by the parallel-processingprocessor 4833, the processor 4831 generates an analysis result of celltypes of each measured cell (step S104).

FIG. 57 shows an operation example of the arithmetic processes of theparallel-processing processor 4833 executed on the basis of aninstruction from the processor 4831 according to operation of theanalysis software 4835 a.

The processor 4831, which executes the analysis software 4835 a, causesthe parallel-processing processor 4833 to execute assignment ofarithmetic processes to the arithmetic units 4836 (step S110). Theprocessor 4831 causes the parallel-processing processor 4833 to executeassignment of arithmetic processes to the arithmetic units 4836 bycalling a kernel function of the parallel-processing processor 4833. Asshown in FIG. 54, for example, a matrix operation regarding the deeplearning algorithm 60 is divided into a plurality of arithmeticprocesses, and the respective divided arithmetic processes are assignedto the arithmetic units 4836. A plurality of pieces of waveform data aresequentially inputted to the deep learning algorithm 60. A matrixoperation corresponding to each piece of waveform data is divided into aplurality of arithmetic processes, to be assigned to the arithmeticunits 4836.

The arithmetic processes are processed in parallel by a plurality of thearithmetic units 4836 (step S111). The arithmetic processes are executedon the plurality of pieces of waveform data.

Arithmetic results generated through the parallel processing by theplurality of the arithmetic units 4836 are transferred from the RAM 4837to the RAM 4834 (step S112). For example, the arithmetic results areDMA-transferred from the RAM 4837 to the RAM 4834.

Configuration Example 2

With reference to FIG. 58 and FIG. 59, another configuration example ofthe cell analyzer 100 composed of the measurement unit 400 and theprocessing unit 300 is described. In the present configuration example2, a parallel-processing processor is provided in the processing unit300.

FIG. 58 shows a block diagram of the measurement unit 400 ofconfiguration example 2.

Except that the measurement unit 400 shown in FIG. 58 is not providedwith the A/D converter 482, the processor 4831, the RAM 4834, thestorage 4835, and the parallel-processing processor 4833, and isprovided with a connection port 4201, the measurement unit 400 shown inFIG. 58 has configurations and functions similar to those of themeasurement unit 400 of configuration example 1 described with referenceto FIG. 1 to FIG. 57 and in related description thereof. A connectioncable 4202 is connected to the connection port 4201.

FIG. 59 shows a block diagram of the processing unit 300 ofconfiguration example 2.

As shown in FIG. 59, the processing unit 300 includes the processor3001, a parallel-processing processor 3002, the storage 3004, a RAM3005, the interface part 3006, an A/D converter 3008, the bus controller4850, and an interface part 3009, and these are connected to the bus3003. That is, in the example in FIG. 59, the parallel-processingprocessor 3002 is installed, in the cell analyzer 100, in the form ofbeing incorporated in the processing unit 300.

The bus 3003 is a transmission line having a data transfer rate of notless than several hundred MB/s, for example. The bus 3003 may be atransmission line having a data transfer rate of not less than 1 GB/s.The bus 3003 performs data transfer in accordance with the PCI-Expressor PCI-X standard, for example. Configurations of the processor 3001,the parallel-processing processor 3002, the storage 3004, and the RAM3005, and processes executed by these are similar to the configurationsand processes of the processor 4831, the parallel-processing processor4833, the storage 4835, and the RAM 4834 described with reference toFIG. 47 to FIG. 57 above. The A/D converter 3008 samples each analogsignal outputted from the measurement unit 400 as described above, andgenerates a digital signal including waveform data of cells. Thegeneration method of the digital signal has been described above.

In the example in FIG. 58 and FIG. 59, the connection cable 4202 isprovided with transmission paths the number of which corresponds to thetypes of analog signals transmitted from the measurement unit 400 to theprocessing unit 300, for example. For example, the connection cable 4202is implemented as a twisted-pair cable, and has pairs of wires, thenumber of pairs corresponding to the types of analog signals transmittedto the processing unit 300. The transmission path from a connection port3007 to the A/D converter 3008 may also have wires of which the numbercorresponds to the types of analog signals transmitted to the processingunit 300. In the transmission path from the connection port 3007 to theA/D converter 3008, the analog signals are transmitted as differentialsignals, for example.

As shown in FIG. 60, the processor 3001 and the parallel-processingprocessor 3002 have configurations and functions similar to those of theprocessor 4831 and the parallel-processing processor 4833 describedabove. The parallel-processing processor 3002 includes a plurality ofarithmetic units 3200 and a RAM 3201. Analysis software 3100 foranalyzing cell types of each measured cell is executed on the processor3001. The parallel-processing processor 3002 may not necessarily bedirectly connected to the bus 3003. For example, the parallel-processingprocessor 3002 may be mounted to a USB device, and this USB device maybe connected to the bus 3003 via an interface part (not shown), wherebythe parallel-processing processor 3002 may be installed to the cellanalyzer 100 as a part of the processing unit 300. This USB device maybe a small device such as a USB dongle, for example.

As shown in FIG. 58 and FIG. 59, the processing unit 300 is connected tothe interface part 489 of the measurement unit 400 via the interfacepart 3006. The processing unit 300 transmits control signals of theapparatus mechanism part 430 and the sample preparation part 440, to themeasurement unit 400 via the interface part 3006. The interface part3006 is a USB interface, for example.

In the example shown in FIG. 58 and FIG. 59, the processing unit 300 isconnected to the connection port 4201 of the measurement unit 400 viathe connection port 3007 connected to the A/D converter 3008 and theconnection cable 4202 connected to the connection port 3007, in additionto the interface part 3006. The connection port 4201 is connected to theanalog processing part 420. Each analog signal outputted from theconnection port 4201 to the processing unit 300 is a signal obtained asa result of the output of the FCM detection part 410 of the measurementunit 400 being processed by the analog processing part 420, as describedabove. The analog processing part 420 performs a process including noiseremoval on the analog signal inputted from the FCM detection part 410.The analog signal having been processed by the analog processing part420 is transmitted to the processing unit 300 via the connection port4201 and the connection cable 4202. The connection cable 4202 is formedto have a length of, for example, 1 meter or less in order to reducenoise during signal transmission. The analog signal is transmitted as adifferential signal to the processing unit 300 via the connection cable4202, for example. In this case, the connection cable 4202 is preferablya twisted-pair cable.

The processing unit 300 may include a plurality of the connection ports3007. The processing unit 300 may obtain analog signals from a pluralityof the measurement units 400 via a plurality of the connection ports3007.

Each analog signal transmitted from the measurement unit 400 via theconnection cable 4202 is converted into a digital signal by the A/Dconverter 3008 of the processing unit 300. As described with referenceto FIG. 5, for example, the A/D converter 3008 samples the analog signaltransmitted at a predetermined sampling rate (e.g., sampling at 1024points at a 10 nanosecond interval, sampling at 128 points at an 80nanosecond interval, sampling at 64 points at a 160 nanosecond interval,or the like), and generates waveform data for each cell. The waveformdata is stored into the storage 3004 or the RAM 3005 via the bus 3003.The waveform data is DMA-transferred to the RAM 3005, for example. Theprocessor 3001 and the parallel-processing processor 3002 executearithmetic processes on the waveform data stored in the storage 3004 orthe RAM 3005.

The analysis software 3100, which operates on the processor 3001, hasfunctions similar to those of the analysis software 4835 a shown in FIG.52. The processor 3001 executes the analysis software 3100, therebygenerating classification information regarding cell types of eachmeasured cell, through operations similar to those described withreference to FIG. 52, FIG. 53A, FIG. 53B, FIG. 54, FIG. 56, and FIG. 57and in related description thereof.

In the case of the cell analyzer 100 of configuration example 2 shown inFIG. 58 and FIG. 59, in the flow chart shown in FIG. 10, generation ofwaveform data and feature parameters in step S12 and cell classificationby the deep learning algorithm in step S13 are performed in theprocessing unit 300. Step S14 (transmission of classificationinformation) is omitted. The processes in FIG. 56 and FIG. 57 areperformed by the processor 3001 and the parallel-processing processor3002 of the processing unit 300.

Configuration Example 3

With reference to FIG. 61 and FIG. 62, another configuration example ofthe cell analyzer 100 composed of the measurement unit 400 and theprocessing unit 300 is described. In the present configuration example 3as well, the parallel-processing processor 3002 is installed, in thecell analyzer 100, in the form of being incorporated in the processingunit 300.

FIG. 61 shows a block diagram of the measurement unit 400 ofconfiguration example 3.

Except that the measurement unit 400 shown in FIG. 61 is not providedwith the processor 4831, the RAM 4834, the storage 4835, and theparallel-processing processor 4833, and is provided with an interfacepart 4851 and a transmission line 4852 for transmitting the digitalsignals generated in the A/D converter 482 to the processing unit 300,the measurement unit 400 shown in FIG. 61 has configurations andfunctions similar to those of the measurement unit 400 of configurationexample 1 described with reference to FIG. 2 and FIG. 47 and in relateddescription thereof.

The interface part 4851 is an interface serving as a dedicated linehaving a communication band of not less than 1 gigabit/second, forexample. For example, the interface part 4851 is an interface accordingto Gigabit Ethernet, USB 3.0, or Thunderbolt 3. When the interface part4851 is according to Gigabit Ethernet, the transmission line 4852 is aLAN cable, for example. When the interface part 4851 is according to USB3.0, the transmission line 4852 is a USB cable according to USB 3.0. Thetransmission line 4852 is a dedicated transmission line for transmittingthe digital signals between the measurement unit 400 and the processingunit 300, for example.

FIG. 62 shows a block diagram of the processing unit 300 ofconfiguration example 3.

Except that the processing unit 300 shown in FIG. 62 is not providedwith the A/D converter 3008 and the connection port 3007, and isprovided with an interface part 3010, the processing unit 300 shown inFIG. 62 has configurations and functions similar to those of theprocessing unit 300 of configuration example 2 described with referenceto FIG. 59 and in related description thereof. The processing unit 300may be connected to a plurality of the measurement units 400 via aplurality of the interface parts 3010 and a plurality of the interfaceparts 3006.

The processor 3001 and the parallel-processing processor 3002 haveconfigurations and functions similar to those of the processor 3001 andthe parallel-processing processor 3002 described with reference to FIG.60 and in related description thereof. In FIG. 62, theparallel-processing processor 3002 may not necessarily be directlyconnected to the bus 3003. For example, the parallel-processingprocessor 3002 may be mounted to a USB device, and this USB device maybe connected to the bus 3003 via an interface part (not shown). This USBdevice may be a small device such as a USB dongle, for example.

The analysis software 3100, which operates on the processor 3001, hasfunctions similar to those of the analysis software 4835 a shown in FIG.52. The analysis software 3100 analyzes cell types of each measuredcell, through operations similar to those described with reference toFIG. 52, FIG. 53A, FIG. 53B, FIG. 54, FIG. 56, and FIG. 57 and inrelated description thereof.

In the case of the cell analyzer 100 of configuration example 3 shown inFIG. 61 and FIG. 62, in the flow chart shown in FIG. 10, step S13 (cellclassification) is performed in the processing unit 300. Step S14(transmission of classification information) is omitted. The processesin FIG. 56 and FIG. 57 are performed by the processor 3001 and theparallel-processing processor 3002 of the processing unit 300.

In the configuration in FIG. 61 and FIG. 62, analog signals (forwardscattered light signal, side scattered light signal, side fluorescencesignal) of each cell generated in the FCM detection part 410 areconverted into digital signals by the A/D converter 482 in themeasurement unit 400. The digital signals are sent to the processingunit 300 via the interface part 484, the bus 485, the interface part4851, and the transmission line 4852. The transmission line 4852 is adedicated transmission line for transmitting the digital signals betweenthe measurement unit 400 and the processing unit 300 as described above.For example, the measurement unit 400 and the processing unit 300 areconnected in a one-to-one relationship via the transmission line 4852.In other words, the transmission line 4852 is a transmission line thatprovides no transmission of data related to an apparatus other thancomponents (e.g., the measurement unit 400 and the processing unit 300)forming the cell analyzer 100, for example. The transmission line 4852is a transmission line different from an intranet or the internet, forexample. Accordingly, even when digital signals generated through A/Dconversion in the measurement unit 400 are transmitted to the processingunit 300, bottleneck in the communication speed of transmission of thedigital signals can be avoided.

Configuration Example 4

With reference to FIG. 63, FIG. 64, FIG. 65, FIG. 66, and FIG. 67,configuration example 4 of the cell analyzer 100 is described.

In the present configuration example 4, as shown as an example in FIG.63, an analysis unit 600 is provided between the measurement unit 400and the processing unit 300. That is, in the configuration in FIG. 63,FIG. 64, FIG. 65, FIG. 66, and FIG. 67, the cell analyzer 100 iscomposed of the measurement unit 400, the processing unit 300, and theanalysis unit 600. The analysis unit 600 analyzes cell types of eachmeasured cell. As described later, in the present configuration example,a parallel-processing processor 6002 is installed, in the cell analyzer100, in the form of being incorporated in the analysis unit 600.

FIG. 64 shows a configuration of the measurement unit 400 ofconfiguration example 4.

The measurement unit 400 shown as an example in FIG. 64 hasconfigurations and functions similar to those of the measurement unit400 of configuration example 3 described with reference to FIG. 61 andin related description thereof. The analysis unit 600 is providedbetween the measurement unit 400 and the processing unit 300. Theanalysis unit 600 may be connected to a plurality of the measurementunits 400. The analysis unit 600 may be connected to a plurality of theprocessing units 300. The interface part 4851 is an interface having acommunication band of not less than 1 gigabit/second, for example. Forexample, the interface part 4851 is an interface according to GigabitEthernet, USB 3.0, or Thunderbolt 3. When the interface part 4851 isaccording to Gigabit Ethernet, the transmission line 4852 is a LANcable, for example. When the interface part 4851 is according to USB3.0, the transmission line 4852 is a USB cable according to USB 3.0. Thetransmission line 4852 is a dedicated transmission line for transmittingthe digital signals between the measurement unit 400 and the processingunit 300 as described above. For example, the measurement unit 400 andthe processing unit 300 are connected in a one-to-one relationship viathe transmission line 4852.

FIG. 65 shows a configuration example of the analysis unit 600.

The analysis unit 600 includes a processor 6001, the parallel-processingprocessor 6002, a bus 6003, a storage 6004, a RAM 6005, an interfacepart 6006, and an interface part 6007, for example, and these areconnected to the bus 6003. The bus 6003 is a transmission line having adata transfer rate of not less than several hundred MB/s, for example.The bus 6003 may be a transmission line having a data transfer rate ofnot less than 1 GB/s. The bus 6003 performs data transfer in accordancewith the PCI-Express or PCI-X standard, for example. The analysis unit600 may be connected to a plurality of the measurement units 400 via aplurality of the interface parts 6006. When a plurality of themeasurement units 400 are provided, an analysis unit 600 may beconnected to each of the measurement units 400 (e.g., a plurality of themeasurement units 400 and a plurality of the analysis units 600 areconnected in a one-to-one relationship).

As shown in FIG. 66, the processor 6001 and the parallel-processingprocessor 6002 have configurations and functions similar to those of theprocessor 4831 and the parallel-processing processor 4833 describedabove. The parallel-processing processor 6002 includes a plurality ofarithmetic units 6200 and a RAM 6201. Analysis software 6100, whichanalyzes cell types of each measured cell, operates on the processor6001. The analysis software 6100, which operates on the processor 6001,has functions similar to those of the analysis software 4835 a shown inFIG. 52. The analysis software 6100 analyzes cell types of each measuredcell through operations similar to those described with reference toFIG. 52, FIG. 53A, FIG. 53B, FIG. 54, FIG. 55A, FIG. 55B, FIG. 56, andFIG. 57, and in related description thereof. The analysis software 6100transmits classification information of each measured cell to theprocessing unit 300 via the interface part 6007. The interface part 6007is of Ethernet (registered trademark) or USB, for example. The interfacepart 6007 may be an interface capable of performing wirelesscommunication (e.g., WiFi (registered trademark), Bluetooth (registeredtrademark)).

FIG. 67 shows a configuration of the processing unit 300 ofconfiguration example 4.

The processing unit 300 shown in FIG. 67 may not necessarily include theparallel-processing processor 3002, unlike the processing unit 300 shownin FIG. 59 and FIG. 62. In addition, the analysis software 3100 shown inFIG. 59 and FIG. 62 may not necessarily operate on the processor 3001shown in FIG. 67. The processing unit 300 receives analysis results ofthe analysis unit 600 via the interface part 3006. The interface part3006 is of Ethernet or USB, for example. The interface part 3006 may bean interface capable of performing wireless communication (e.g., WiFi,Bluetooth).

In the configuration in FIG. 64, FIG. 65, FIG. 66, and FIG. 67, analogsignals (forward scattered light signal, side scattered light signal,side fluorescence signal) of each cell generated in the FCM detectionpart 410 are converted into digital signals by the A/D converter 482 inthe measurement unit 400. Waveform data is sent to the analysis unit 600via the interface part 484, the bus 485, the interface part 4851, andthe transmission line 4852. The interface part 4851 is a dedicatedinterface that connects the measurement unit 400 and the analysis unit600 to each other as described above, and the interface part 4851connects the measurement unit 400 and the analysis unit 600 in aone-to-one relationship. In other words, the transmission line 4852 is atransmission line that provides no transmission of data related to anapparatus other than components (e.g., the measurement unit 400 and theprocessing unit 300) forming the cell analyzer 100, for example. Thetransmission line 4852 is a transmission line different from an intranetor the internet, for example. Accordingly, even when digital signalsgenerated through A/D conversion in the measurement unit 400 aretransmitted to the processing unit 300, bottleneck in the communicationspeed of transmission of the digital signals can be avoided.

In the case of the cell analyzer 100 of configuration example 4 shown inFIG. 64, FIG. 65, FIG. 66, and FIG. 67, in the flow chart shown in FIG.10, step S13 (cell classification) and step S14 (transmission ofclassification information) are performed in the analysis unit 600. Thatis, the digital signals generated in step S13 are transmitted from themeasurement unit 400 to the analysis unit 600, and classificationinformation is transmitted to the processing unit 300 in step S14. Theprocesses in FIG. 56 and FIG. 57 are performed by the processor 6001 andthe parallel-processing processor 6002 of the analysis unit 600.

Configuration Example 5

With reference to FIG. 63, FIG. 67, and FIG. 68, configuration example 5of the cell analyzer 100 is described.

The cell analyzer 100 of this configuration example 5 also includes themeasurement unit 400, the processing unit 300, and the analysis unit600, as in the case of configuration example 4 described above. Themeasurement unit 400 of configuration example 5 shown in FIG. 68 hasfunctions and configurations similar to those of the measurement unit400 described with reference to FIG. 58 and in related descriptionthereof. The measurement unit 400 of configuration example 5 shown inFIG. 68 is connected to the analysis unit 600 via the connection cable4202. For example, the connection cable 4202 is implemented as atwisted-pair cable, and has pairs of wires, the number of pairscorresponding to the types of analog signals transmitted to theprocessing unit 300. The connection cable 4202 is formed to have alength of, for example, 1 meter or less in order to reduce noise duringsignal transmission. The measurement unit 400 transmits analog signalsto the analysis unit 600 via the connection cable 4202.

The analysis unit 600 shown in FIG. 69 has functions and configurationssimilar to those of the analysis unit 600 described with reference toFIG. 65 and in related description thereof. That is, in the example inFIG. 69, the parallel-processing processor 6002 is installed, in thecell analyzer 100, in the form of being incorporated in the analysisunit 600. The analysis unit 600 shown in FIG. 69 further includes aconnection port 6008 and an A/D converter 6009. Analog signalstransmitted from the measurement unit 400 via the connection cable 4202are inputted to the A/D converter 6009 via the connection port 6008. TheA/D converter 6009 converts the analog signals into digital signalsthrough a process similar to that performed by the A/D converter 482.

The analysis unit 600 may be connected to a plurality of the measurementunits 400 via a plurality of the connection ports 6008. When a pluralityof the measurement units 400 are provided, an analysis unit 600 may beconnected to each of the measurement units 400 (e.g., a plurality of themeasurement units 400 and a plurality of the analysis units 600 areconnected in a one-to-one relationship).

As shown in FIG. 66, the processor 6001 and the parallel-processingprocessor 6002 have configurations and functions similar to those of theprocessor 4831 and the parallel-processing processor 4833 describedabove. The analysis software 6100, which analyzes cell types of eachmeasured cell, operates on the processor 6001. The analysis software6100, which operates on the processor 6001, has functions similar tothose of the analysis software 4835 a shown in FIG. 52. The analysissoftware 6100 analyzes cell types of each measured cell throughoperations similar to those described with reference to FIG. 52, FIG.53A, FIG. 53B, FIG. 54, FIG. 55A, FIG. 55B, FIG. 56, and FIG. 57, and inrelated description thereof. The analysis software 6100 transmitsanalysis results of cell types of each measured cell to the processingunit 300 via the interface part 6007. The interface part 6007 is ofEthernet or USB, for example. The interface part 6007 may be aninterface capable of performing wireless communication (e.g., WiFi,Bluetooth).

In the case of the cell analyzer 100 of configuration example 5 shown inFIG. 68 and FIG. 69, in the flow chart shown in FIG. 10, generation ofwaveform data and feature parameters in step S12, step S13 (cellclassification), and step S14 (transmission of classificationinformation) are performed in the analysis unit 600. That is, generationof waveform data (step S12) is performed in the A/D converter 6009 ofthe analysis unit 600, cell classification based on digital signals(step S13) is performed by the processor 6001 and theparallel-processing processor 6002 of the analysis unit 600, andclassification information is transmitted from the analysis unit 600 tothe processing unit 300 (step S14). The processes in FIG. 56 and FIG. 57are performed by the processor 6001 and the parallel-processingprocessor 6002 of the analysis unit 600.

9. Neural Network Structure and Training of Deep Learning Algorithm 60(Structure of Neural Network)

FIG. 70A shows, as an example, a structure of a convolution neuralnetwork which realizes the deep learning algorithm 60.

The neural network includes an input layer 60 a, an output layer 60 b,and a middle layer 60 c between the input layer 60 a and the outputlayer 60 b. The middle layer 60 c is composed of a plurality of layers.The number of layers forming the middle layer 60 c may be, for example,not less than 5, preferably not less than 50, and more preferably notless than 100.

In the neural network, a plurality of nodes 89 arranged in a layeredmanner are connected between the layers. Accordingly, information ispropagated only in one direction indicated by an arrow D in the drawing,from the input-side input layer 60 a to the output-side the output layer60 b.

(Arithmetic Operation at Each Node)

FIG. 70B is a schematic diagram showing arithmetic operations performedat each node.

Each node 89 receives a plurality of inputs, and calculates one output(z). In the case of the example shown in FIG. 70B, the node 89 receivesfour inputs. The total input (u) received by the node 89 is expressed byFormula 2 below, for example. In the present embodiment, one-dimensionalmatrix data is used as training input data and analysis input data.Therefore, when variables of the arithmetic expression correspond totwo-dimensional matrix data, a process of converting the variables so asto correspond to one-dimensional matrix data is performed.

u=w ₁ x ₁ +w ₂ x ₂ +w ₃ x ₃ +w ₄ x ₄ +b  (Formula 2)[Math 2]

Each input is multiplied by a different weight. In Formula 2, b is avalue called bias. The output (z) of the node serves as an output of apredetermined function f with respect to the total input (u) expressedby Formula 2, and is expressed by Formula 3 below. The function f iscalled an activation function.

z=f(u)  (Formula 3)[Math 3]

FIG. 70C is a schematic diagram showing arithmetic operations betweennodes.

In the neural network, with respect to the total input (u) of each node89 expressed by Formula 2, nodes that output results (z) each expressedby Formula 3 are arranged in a layered manner. Outputs of the nodes ofthe previous layer serve as inputs to nodes of the next layer. In theexample shown in FIG. 70C, the outputs from nodes 89 a in the left layerin FIG. 70C serve as inputs to nodes 89 b in the right layer. Each node89 b receives outputs from the nodes 89 a. The connection between eachnode 89 a and each node 89 b is multiplied by a different weight. Whenthe respective outputs from the plurality of nodes 89 a are defined asx1 to x4, the inputs to the respective three nodes 89 b are expressed byFormula 4-1 to Formula 4-3 below.

u ₁ =w ₁₁ x ₁ +w ₁₂ x ₂ +w ₁₃ x ₃ +w ₁₄ x ₄ +b ₁  (Formula 4-1)

u ₂ =w _(21×1) +w ₂₂ x ₂ +w ₂₃ x ₃ +w ₂₄ x ₄ +b ₂  (Formula 4-2)

u ₃ =w ₃₁ x ₁ +w ₃₂ x ₂ +w ₃₃ x ₃ +w ₃₄ x ₄ +b ₃  (Formula 4-3)[Math 4]

When Formula 4-1 to Formula 4-3 are generalized, Formula 4-4 below isobtained. Here, i=1, . . . , I,j=1, . . . , J. I is the total number ofinputs, and J is the total number of outputs.

u _(j)=Σ_(i=1) ^(I) w _(ji) x _(i) +b _(j)  (Formula 4-4)[Math 5]

When Formula 4-4 is applied to the activation function, an output isobtained. The output is expressed by Formula 5 below.

z _(j)=ƒ(u _(j))(j=1,2,3)  (Formula 5)[Math 6]

(Activation Function)

In the cell type analysis method according to the embodiment, arectified linear unit function is used as the activation function. Therectified linear unit function is expressed by Formula 6 below.

ƒ(u)=max(u,0)  (Formula 6)[Math 7]

Formula 6 is a function obtained by setting u=0 to the part u<0 in thelinear function with z=u. In the example shown in FIG. 70C, usingFormula 6, the output from the node of j=1 is expressed by the formulabelow.

z ₁=max((w ₁₁ x ₁ +w ₁₂ x ₂ +w ₁₃ x ₃ +w ₁₄ x ₄ +b ₁),0)  [Math 8]

(Neural Network Learning)

If the function expressed by use of a neural network is defined asy(x:w), the function y(x:w) varies when a parameter w of the neuralnetwork is varied. Adjusting the function y(x:w) such that the neuralnetwork selects a more suitable parameter w with respect to the input xis referred to as neural network learning. It is assumed that aplurality of pairs of an input and an output of the function expressedby use of the neural network have been provided. If a desirable outputfor an input x is defined as d, the pairs of the input/output are givenas {(x1,d1), (x2,d2), . . . , (xn,dn)}. The set of pairs each expressedas (x,d) is referred to as training data.

The neural network learning means adjusting the weight w by use of anerror function such that, with respect to any input/output pair (xn,dn),the output y(xn:w) of the neural network when given an input xn becomesas close to the output dn as much as possible.

y(xn:w)≈n  [Math 9]

The error function is a measure for the closeness between the trainingdata and the function expressed by use of the neural network. The errorfunction is also called a loss function. An error function E(w) used inthe cell type analysis method according to the embodiment is expressedby Formula 7 below. Formula 7 is called cross entropy.

E(w)=−Σ_(n=1) ^(N)Σ_(k=1) ^(K) d _(nk) log y _(k)(xn:W)  (Formula7)[Math 10]

A method for calculating the cross entropy of Formula 7 is described. Inthe output layer of the neural network used in the cell type analysismethod according to the embodiment, i.e., in the last layer of theneural network, an activation function for classifying inputs x into afinite number of classes according to the contents, is used. Theactivation function is called a softmax function, and expressed byFormula 8 below. It is assumed that, in the output layer 60 b, the nodesare arranged by the same number as the number of classes k. It isassumed that the total input u of each node k (k=1, . . . , K) of anoutput layer L is given as uk^((L)) from the outputs of the previouslayer L−1. Accordingly, the output of the k-th node in the output layeris expressed by Formula 8 below.

$\begin{matrix}\left\lbrack {{Math}11} \right\rbrack &  \\{{y_{k} \equiv z_{k}^{(L)}} = \frac{\exp\left( u_{k}^{(L)} \right)}{\sum_{j = 1}^{K}{\exp\left( u_{j}^{(L)} \right)}}} & \left( {{Formula}8} \right)\end{matrix}$

Formula 8 is the softmax function. The sum of output y1, . . . , ykdetermined by Formula 8 is always 1.

When each class is expressed as C1, . . . , Ck, output yk of node k inthe output layer L (i.e., uk^((L))) represents the probability that thegiven input x belongs to class Ck. The input x is classified into aclass in which the probability expressed by Formula 9 below becomeslargest.

p(C _(k) |x)=y _(k) =z _(k) ^((L))  (Formula 9)[Math 12]

In the neural network learning, a function expressed by the neuralnetwork is considered as a model of the posterior probability of eachclass, the likelihood of the weight w with respect to the training datais evaluated under such a probability model, and a weight w thatmaximizes the likelihood is selected.

It is assumed that target output dn by the softmax function of Formula 8is 1 only if the output is a correct class, and otherwise, target outputdn is 0. When the target output is expressed in a vector format dn=[dn1,. . . , dnk], if, for example, the correct class of input xn is C3, onlytarget output dn3 becomes 1, and the other target outputs become 0. Whencoding is performed in this manner, the posterior distribution isexpressed by Formula 10 below.

p(d|x)=Π_(k=1) ^(K) p(C _(k) |x)^(d) ^(k)   (Formula 10)[Math 13]

Likelihood L(w) of weight w with respect to the training data{(xn,dn)}(n=1, . . . , N) is expressed by Formula 11 below. When thelogarithm of likelihood L(w) is taken and the sign is inverted, theerror function of Formula 7 is derived.

$\begin{matrix}\left\lbrack {{Math}14} \right\rbrack &  \\\begin{matrix}{{L(w)} = {\prod_{n = 1}^{N}{p\left( {d_{n}❘{x_{n}:w}} \right)}}} \\{= {\prod_{n = 1}^{N}{\prod_{k = 1}^{K}{p\left( {C_{k}❘x_{n}} \right)}^{d_{nk}}}}} \\{= {\prod_{n = 1}^{N}{\prod_{k = 1}^{K}\left( {y_{k}\left( {x:w} \right)} \right)^{d_{nk}}}}}\end{matrix} & \left( {{Formula}11} \right)\end{matrix}$

Learning means minimizing the error function E(w) calculated on thebasis of the training data, with respect to parameter w of the neuralnetwork. In the cell type analysis method according to the embodiment,the error function E(w) is expressed by Formula 7.

Minimizing the error function E(w) with respect to parameter w has thesame meaning as finding a local minimum point of the function E(w).Parameter w is a weight of connection between nodes. The local minimumpoint of weight w is obtained by iterative calculation of repeatedlyupdating parameter w from an arbitrary initial value used as a startingpoint. An example of such calculation is the gradient descent method.

In the gradient descent method, a vector expressed by Formula 12 belowis used.

$\begin{matrix}\left\lbrack {{Math}15} \right\rbrack &  \\{{\nabla E} = {\frac{\partial E}{\partial w} = \left\lbrack {\frac{\partial E}{\partial w_{1}},\ldots,\frac{\partial E}{\partial w_{M}}} \right\rbrack^{T}}} & \left( {{Formula}12} \right)\end{matrix}$

In the gradient descent method, a process of moving the value of thecurrent parameter w in the negative gradient direction (i.e., −∇E) isrepeated many times. When the current weight is w^((t)) and the weightafter the moving is w^((t+1)), the arithmetic operation according to thegradient descent method is expressed by Formula 13 below. Value t meansthe number of times the parameter w is moved.

w ^((t+1)) =w ^((t)) −∈∇E  (Formula 13)[Math 16]

The symbol shown in Formula 14 below used in Formula 13 is a constantthat determines the magnitude of the update amount of parameter w, andis called a learning coefficient.

∈  (Formula 14)[Math 17]

As a result of repetition of the arithmetic operation expressed byFormula 13, error function E(w^((t))) decreases in association withincrease of value t, and parameter w reaches a local minimum point.

It should be noted that the arithmetic operation according to Formula 13may be performed on all of the training data (n=1, . . . , N) or may beperformed on only a part of the training data. The gradient descentmethod performed on only a part of the training data is called astochastic gradient descent method. In the cell type analysis methodaccording to the embodiment, the stochastic gradient descent method isused.

An outline of training of the deep learning algorithm 60 is describedusing FIG. 71 as an example. As described above, the deep learningalgorithm 60 is implemented as a neural network that includes amulti-layered middle layer. As the training data, waveform data of acell of which the cell type has been identified in advance is inputtedto the input layer 60 a of the neural network. As the training data,probability data 78 is inputted to the output layer 60 b of the neuralnetwork. When the number of cell types to be classified by the deeplearning algorithm 60 is nine as shown in FIG. 8, the number of nodes ofthe output layer 60 b is nine, and one of the cell types is assigned toeach node. The probability data 78 is a data group in which theprobability of the label value of the cell type that has been identifiedin advance and that corresponds to the waveform data inputted to theinput layer 60 a is set to 100% and the probabilities of the other celltypes are set to 0%. In this manner, the training data is inputted toeach of the input layer 60 a and the output layer 60 b, whereby theneural network is trained.

10. Effects of Embodiment

According to the embodiment described above, as representatively shownin FIG. 20, for example, a result in which each individual cell isclassified into a plurality of cell types is displayed. In aconventional cell classification method, a cell that has features of aneutrophil and an immature granulocyte, for example, is alternativelyclassified into either one of these types. However, in the embodimentdescribed above, with respect to such a cell, the probabilities ofbelonging to neutrophil and immature granulocyte are obtained by thedeep learning algorithm, and the values of the probabilitiescorresponding to the respective cell types are displayed. Thus, the usercan take an action of conducting more detailed analysis such as, forexample, performing a retest or a visual inspection for detection ofabnormal cells, on a specimen containing a cell that has a probabilityof being an abnormal cell.

Further, according to the embodiment described above, when the user hasconfirmed the result as shown in FIG. 20, and then performs a validationoperation, output data as representatively shown in FIG. 44 isgenerated, and transmitted to the host computer. In the output data, atleast a part of the generated classification information is removed, andthus, increase in the communication load to the host computer can beavoided.

11. Remarks

The present disclosure includes following items 1-20.

Item 1: An analysis method for analyzing a specimen containing cells,the analysis method comprising: applying light to a measurement sampleprepared from the specimen and detecting light generated from cells;obtaining, with respect to each of a plurality of cells contained in thespecimen, feature data of the cell on the basis of the detected light;analyzing the feature data with use of an artificial intelligencealgorithm, thereby classifying each of the cells into a plurality ofcell types; and displaying information based on a result of theclassifying.

Item 2: The analysis method of item 1, wherein the classifying includesspecifying, on the basis of the feature data, a plurality of cell typesto each of which each of the cells has a possibility of morphologicallybelonging, among a plurality of cell types determined in advance.

Item 3: The analysis method of item 2, wherein the plurality of celltypes include at least one normal cell and at least one abnormal cell,and the classifying includes obtaining a probability that each of thecells belongs to the abnormal cell.

Item 4: The analysis method of any one of items 1 to 3, wherein theplurality of cell types include lymphocyte, monocyte, eosinophil,neutrophil, basophil, and abnormal blood cell.

Item 5: The analysis method of item 3 or 4, wherein the abnormal bloodcell includes at least one of immature granulocyte, blast, and abnormallymphocyte.

Item 6: The analysis method of any one of items 1 to 5, furthercomprising counting cells on the basis of the result of the classifyingand displaying a result of the counting of the cells.

Item 7: The analysis method of any one of items 1 to 6, wherein theanalyzing of the data includes inputting the feature data of one cellinto a deep learning algorithm, and obtaining, as an output from thedeep learning algorithm, a result of classifying the cell into aplurality of cell types.

Item 8: The analysis method of item 7, wherein the deep learningalgorithm has been trained using, as teaching data, feature data ofcells and information regarding types of the cells.

Item 9: The analysis method of any one of items 1 to 8, wherein thedetecting includes detecting light generated as a result of a cellpassing through a flow cell to which light is applied, and the obtainingof the feature data includes obtaining a waveform signal that changesover time in accordance with the detected light.

Item 10: The analysis method of item 9, wherein the feature data iswaveform data generated by sampling the waveform signal at a pluralityof time points.

Item 11: The analysis method of any one of items 1 to 10, wherein theanalyzing of the data is performed by using a processor and aparallel-processing processor that operates under an order of theprocessor.

Item 12: The analysis method of any one of items 1 to 11, furthercomprising: displaying a graph on which a plurality of cells containedin the specimen are plotted on the basis of the feature data; anddisplaying, in accordance with one or a plurality of cells having beenselected on the graph, a result of classifying the selected cell.

Item 13: The analysis method of any one of items 1 to 12, wherein theanalyzing of the data includes obtaining a probability that one cellcorresponds to each of a plurality of the cell types.

Item 14: The analysis method of item 13, wherein the displaying of theresult of the classifying includes displaying the probability.

Item 15: The analysis method of item 14, wherein the displaying of theresult of the classifying includes displaying a cell type that has ahighest probability.

Item 16: The analysis method of item 15, wherein displaying ofinformation based on the result of the classifying includes displayingstatistic information generated on the basis of the probability.

Item 17: The analysis method of any one of items 1 to 16, furthercomprising: displaying a graph on which a plurality of cells containedin the specimen are plotted on the basis of the feature data; anddisplaying, in accordance with one or a plurality of cells having beenselected on the graph, a result of classifying the selected one orplurality of cells.

Item 18: The analysis method of any one of items 1 to 17, furthercomprising: displaying a graph on which a plurality of cells containedin the specimen are plotted on the basis of the feature data; anddisplaying, in accordance with a plurality of cells having been selectedon the graph, statistic information based on a result of classifying theselected plurality of cells.

Item 19: The analysis method of any one of items 1 to 18, furthercomprising: displaying a graph on which a plurality of cells containedin the specimen are plotted on the basis of the feature data; receivingan input of an extraction condition of a cell; extracting a cell thatsatisfies the extraction condition on the basis of classificationinformation; and displaying, among the cells displayed on the graph, theextracted cell so as to be distinguished from other cells.

Item 20: An analyzer configured to analyze a specimen containing cells,the analyzer comprising: a sample preparation part configured to preparea measurement sample from the specimen; a detection part configured toapply light to the measurement sample prepared by the sample preparationpart, and to detect light generated from cells; a signal processing partconfigured to obtain, with respect to each of a plurality of cellscontained in the specimen, feature data of the cell on the basis of thedetected light; a display part; and at least one processor configured toanalyze the feature data with use of an artificial intelligencealgorithm, thereby classifying each of the cells into a plurality ofcell types, the at least one processor being configured to cause thedisplay part to display information based on a result of theclassifying.

What is claimed is:
 1. An analysis method for a specimen using ananalyzer connected to a host computer, the analysis method comprising:obtaining, with respect to each of a plurality of cells contained in thespecimen, feature data of the cell; generating classificationinformation in which each of the cells is classified into a plurality ofcell types, by analyzing the feature data with use of an artificialintelligence algorithm and performing classifying; generating ameasurement result of the specimen on the basis of the classificationinformation; displaying, on a display part of the analyzer, themeasurement result and at least a part of the classificationinformation; and transmitting, to the host computer, output data thatincludes the measurement result and in which at least a part of theclassification information has been removed.
 2. The analysis method ofclaim 1, wherein the output data includes a part of the classificationinformation.
 3. The analysis method of claim 1, wherein theclassification information includes a probability that one cellcorresponds to each of a plurality of cell types, and the output dataincludes a probability that corresponds to a cell type that has ahighest probability.
 4. The analysis method of claim 1, wherein theclassification information includes a probability that one cellcorresponds to each of a plurality of cell types, and the output dataincludes probabilities that respectively correspond to cell types thathave highest two probabilities.
 5. The analysis method of claim 1,wherein the output data includes a probability that a cell correspondsto an abnormal cell.
 6. The analysis method of claim 1, wherein theabnormal cell is a cell other than a monocyte, a lymphocyte, aneutrophil, an eosinophil, and a basophil.
 7. The analysis method ofclaim 6, wherein the abnormal cell includes an abnormal lymphocyte, ablast, and an immature granulocyte.
 8. The analysis method of claim 1,wherein the output data is transmitted to the host computer inaccordance with a fact that the measurement result has been validated.9. The analysis method of claim 1, wherein the measurement resultincludes a result of counting cells on the basis of the classificationinformation.
 10. The analysis method of claim 1, wherein the artificialintelligence algorithm is a deep learning algorithm.
 11. The analysismethod of claim 10, wherein the classifying includes inputting the dataof one cell into the deep learning algorithm, and obtaining, as anoutput from the deep learning algorithm, a result of classifying thecell into a plurality of cell types.
 12. The analysis method of claim11, wherein the deep learning algorithm has been trained using, asteaching data, feature data regarding morphology of cells andinformation regarding types of the cells.
 13. The analysis method ofclaim 1, wherein the classifying is performed by using a processor and aparallel-processing processor that operates under an order of theprocessor.
 14. An analyzer comprising: a measurement unit configured to,with respect to each of a plurality of cells contained in a specimen,obtain feature data of the cell; a processor configured to generateclassification information in which each of the cells is classified intoa plurality of cell types, by analyzing the feature data with use of anartificial intelligence algorithm and performing classifying, theprocessor being configured to generate a measurement result of aspecimen on the basis of the classification information; and a displaypart configured to display the measurement result and at least a part ofthe classification information, wherein the processor transmits, to ahost computer connected to the analyzer, output data that includes themeasurement result and in which at least a part of the classificationinformation has been removed.
 15. The analyzer of claim 14, wherein theoutput data includes a part of the classification information.
 16. Theanalyzer of claim 14, wherein the classification information includes aprobability that one cell corresponds to each of a plurality of celltypes, and the output data includes a probability that corresponds to acell type that has a highest probability.
 17. The analyzer of claim 14,wherein the classification information includes a probability that onecell corresponds to each of a plurality of cell types, and the outputdata includes probabilities that respectively correspond to cell typesthat have highest two probabilities.
 18. The analyzer of claim 14,wherein the output data includes a probability that a cell correspondsto an abnormal cell.
 19. The analyzer of claim 14, wherein the abnormalcell is a cell other than a monocyte, a lymphocyte, a neutrophil, aneosinophil, and a basophil.
 20. The analyzer of claim 19, wherein theabnormal cell includes an abnormal lymphocyte, a blast, and an immaturegranulocyte.
 21. The analyzer of claim 14, wherein the processortransmits, to the host computer, the output data in accordance with afact that the measurement result has been validated.
 22. The analyzer ofclaim 14, wherein the measurement result includes a result of countingcells on the basis of the classification information.
 23. The analyzerof claim 14, wherein the artificial intelligence algorithm is a deeplearning algorithm.
 24. The analyzer of claim 23, wherein theclassifying includes inputting the data of one cell into the deeplearning algorithm, and obtaining, as an output from the deep learningalgorithm, a result of classifying the cell into a plurality of celltypes.
 25. The analyzer of claim 24, wherein the deep learning algorithmhas been trained using, as teaching data, data regarding morphology ofcells and information regarding types of the cells.
 26. The analyzer ofclaim 14, wherein the classifying is performed by using a processor anda parallel-processing processor that operates under an order of theprocessor.